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Big Data Project Ideas Big Data is an exciting subject. It helps you find patterns and results you wouldn’t have noticed otherwise. This skill highly in demand, and you can quickly advance your career by learning it. So, if you are a big data beginner, the best thing you can do is work on some big data project ideas. But it can be difficult for a beginner to find suitable big data topics as they aren’t very familiar with the subject.  We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting big data project ideas which beginners can work on to put their big data knowledge to test. In this article, you will find top big data project ideas for beginners to get hands-on experience on big data Check out our free courses to get an edge over the competition. However, knowing the theory of big data alone won’t help you much. You’ll need to practice what you’ve learned. But how would you do that? You can practice your big data skills on big data projects. Projects are a great way to test your skills. They are also great for your CV. Especially big data research projects and data processing projects are something that will help you understand the whole of the subject most efficiently.  Read: Big data career path You won’t belive how this Program Changed the Career of Students Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses What are the areas where big data analytics is used? Before jumping into the list of  big data topics that you can try out as a beginner, you need to understand the areas of application of the subject. This will help you invent your own topics for data processing projects once you complete a few from the list. Hence, let’s see what are the areas where big data analytics is used the most. This will help you navigate how to identify issues in certain industries and how they can be resolved with the help of big data as big data research projects. Banking and Safety: The banking industry often deals with cases of card fraud, security fraud, ticks and such other issues that greatly hamper their functioning as well as market reputation. Hence to tackle that, the securities exchange commission aka SEC takes the help of big data and monitors the financial market activity.  This has further helped them manage a safer environment for highly valuable customers like retail traders, hedge funds, big banks and other eminent individuals in the financial market. Big data has helped this industry in the cases like anti-money laundering, fraud mitigation, demand enterprise risk management and other cases of risk analytics.  Media and Entertainment industry It is needless to say that the media and entertainment industry heavily depends on the verdict of the consumers and this is why they are always required to put up their best game. For that, they require to understand the current trends and demands of the public, which is also something that changes rapidly these days. To get an in-depth understanding of consumer behaviour and their needs, the media and entertainment industry collects, analyses and utilises customer insights. They leverage mobile and social media content to understand the patterns at a real-time speed.  The industry leverages Big data to run detailed sentiment analysis to pitch the perfect content to the users. Some of the biggest names in the entertainment industry such as Spotify and Amazon Prime are known for using big data to provide accurate content recommendations to their users, which helps them improve their customer satisfaction and, therefore, increases customer retention.  Healthcare Industry Even though the healthcare industry generates huge volumes of data on a daily basis which can be ustilised in many ways to improve the healthcare industry, it fails to utilise it completely due to issues of usability of it. Yet there is a significant number of areas where the healthcare industry is continuously utilising Big Data. The main area where the healthcare industry is actively leveraging big data is to improve hospital administration so that patients can revoke best-in-class clinical support. Apart from that, Big Data is also used in fighting lethal diseases like cancer. Big Data has also helped the industry to save itself from potential frauds and committing usual man-made errors like providing the wrong dosage, medicine etc.  Education Similar to the society that we live in, the education system is also evolving. Especially after the pandemic hit hard, the change became even more rapid. With the introduction of remote learning, the education system transformed drastically, and so did its problems. On that note, Big Data significantly came in handy, as it helped educational institutions to get the insights that can be used to take the right decisions suitable for the circumstances. Big Data helped educators to understand the importance of creating a unique and customised curriculum to fight issues like students not being able to retain attention.  It not only helped improve the educational system but to identify the student’s strengths and channeled them right.  Government and Public Services Likewise the field of government and public services itself, the applications of Big Data by them are also extensive and diverse. Government leverages big data mostly in areas like financial market analysis, fraud detection, energy resource exploration, environment protection, public-health-related research and so forth.  The Food and Drug Administration (FDA) actively uses Big Data to study food-related illnesses and disease patterns.  Retail and Wholesale Industry In spite of having tons of data available online in form of reviews, customer loyalty cards, RFID etc. the retail and wholesale industry is still lacking in making complete use of it. These insights hold great potential to change the game of customer experience and customer loyalty.  Especially after the emergence of e-commerce, big data is used by companies to create custom recommendations based on their previous purchasing behaviour or even from their search history.  In the case of brick-and-mortar stores as well, big data is used for monitoring store-level demand in real-time so that it can be ensured that the best-selling items remain in stock. Along with that, in the case of this industry, data is also helpful in improving the entire value chain to increase profits.   Manufacturing and Resources Industry The demand for resources of every kind and manufactured product is only increasing with time which is making it difficult for industries to cope. However, there are large volumes of data from these industries that are untapped and hold the potential to make both industries more efficient, profitable and manageable.  By integrating large volumes of geospatial and geographical data available online, better predictive analysis can be done to find the best areas for natural resource explorations. Similarly, in the case of the manufacturing industry, Big Data can help solve several issues regarding the supply chain and provide companies with a competitive edge.  Insurance Industry  The insurance industry is anticipated to be the highest profit-making industry but its vast and diverse customer base makes it difficult for it to incorporate state-of-the-art requirements like personalized services, personalised prices and targeted services. To tackle these prime challenges Big Data plays a huge part. Big data helps this industry to gain customer insights that further help in curating simple and transparent products that match the recruitment of the customers. Along with that, big data also helps the industry analyse and predict customer behaviours and results in the best decision-making for insurance companies. Apart from predictive analytics, big data is also utilised in fraud detection.  How do you create a big data project? Creating a big data project involves several key steps and considerations. Here’s a general outline to guide you through the process: Define Objectives: Clearly define the objectives and goals of your big data project. Identify the business problems you want to solve or the insights you aim to gain from the data. Data Collection: Determine the sources of data you need for your project. It could be structured data from databases, unstructured data from social media or text documents, or semi-structured data from log files or XML. Plan how you will collect and store this data. Data Storage: Choose a suitable storage solution for your data. Depending on the volume and variety of data, you may consider traditional databases, data lakes, or distributed file systems like Hadoop HDFS. Data Processing: Determine how you will process and manage your big data. This step usually involves data cleaning, transformation, and integration. Technologies like Apache Spark or Apache Hadoop MapReduce are commonly used for large-scale data processing. Data Analysis: Perform exploratory data analysis to gain insights and understand patterns within the data. Use data visualization tools to present the findings. Implement Algorithms: If your project involves machine learning or advanced analytics, implement relevant algorithms to extract meaningful information from the data. Performance Optimization: Big data projects often face performance challenges. Optimize your data processing pipelines, algorithms, and infrastructure for efficiency and scalability. Data Security and Privacy: Ensure that your project adheres to data security and privacy regulations. Implement proper data access controls and anonymization techniques if needed. Deploy and Monitor: Deploy your big data project in a production environment and set up monitoring to track its performance and identify any issues. Evaluate Results: Continuously evaluate the results of your big data project against the defined objectives. Refine and improve your approach based on feedback and insights gained from the project. Documentation: Thoroughly document each step of the project, including data sources, data processing steps, analysis methodologies, and algorithms used. This documentation will be valuable for future reference and for collaborating with others. Team Collaboration: Big data projects often involve collaboration between various teams, such as data engineers, data scientists, domain experts, and IT professionals. Effective communication and collaboration are crucial for the success of the project. The Key Elements of a Good Big Data Project Before you learn about different big data projects, you should understand the criteria for evaluating them: Quality Over Quantity In the field of big data, it is a common tendency to prioritize quantity. However, quality should be a major focus while selecting data to analyze. The ultimate goal of big data analysis is nothing different from other analytical tasks. It involves driving important insights to fulfill business objectives and make major decisions. So, it’s extremely crucial to collect data from the right sources for analysis. You can explore different resources before finding the absolute best for collecting data. Additionally, you will have to find the right algorithms for processing data and interpreting everything accurately. Concentrate on Outcome and Impact The purpose of big data projects is to meet several business objectives. So, your focus shouldn’t be on using more data or utilizing more tools to perform big data analysis. Instead, you should improve the impact of big data projects to allow organizations to develop better strategies. Clean Code and Analysis This aspect of big data projects will depend on your work mechanism as an individual or a team. It’s extremely vital to generate clean code. Therefore, your code should be formatted in the right way and contain comments in the necessary places. A clean code makes it easy to proceed with big data projects. Even your colleagues will find it easier to proceed with the project at a later point when you might not be available. While writing code for data analysis, rely on fair and goal-oriented methodologies. Emotions and biases can easily mess with the accuracy of your data analysis. So, you should stay away from these mistakes while writing code for big data projects. What problems you might face in doing Big Data Projects Big data is present in numerous industries. So you’ll find a wide variety of big data project topics to work on too. Apart from the wide variety of project ideas, there are a bunch of challenges a big data analyst faces while working on such projects. They are the following: Limited Monitoring Solutions You can face problems while monitoring real-time environments because there aren’t many solutions available for this purpose. That’s why you should be familiar with the technologies you’ll need to use in big data analysis before you begin working on a project. Timing Issues A common problem among data analysis is of output latency during data virtualization. Most of these tools require high-level performance, which leads to these latency problems. Due to the latency in output generation, timing issues arise with the virtualization of data. The requirement of High-level Scripting When working on big data analytics projects, you might encounter tools or problems which require higher-level scripting than you’re familiar with. In that case, you should try to learn more about the problem and ask others about the same. Data Privacy and Security While working on the data available to you, you have to ensure that all the data remains secure and private. Leakage of data can wreak havoc to your project as well as your work. Sometimes users leak data too, so you have to keep that in mind. Knowledge Read: Big data jobs & Career planning Unavailability of Tools You can’t do end-to-end testing with just one tool. You should figure out which tools you will need to use to complete a specific project. When you don’t have the right tool at a specific device, it can waste a lot of time and cause a lot of frustration. That is why you should have the required tools before you start the project. Check out big data certifications at upGrad Too Big Datasets You can come across a dataset which is too big for you to handle. Or, you might need to verify more data to complete the project as well. Make sure that you update your data regularly to solve this problem. It’s also possible that your data has duplicates, so you should remove them, as well. While working on big data projects, keep in mind the following points to solve these challenges:         Use the right combination of hardware as well as software tools to make sure your work doesn’t get hampered later on due to the lack of the same.         Check your data thoroughly and get rid of any duplicates.         Follow Machine Learning approaches for better efficiency and results.         What are the technologies you’ll need to use in Big Data Analytics Projects: We recommend the following technologies for beginner-level big data projects:         Open-source databases         C++, Python         Cloud solutions (such as Azure and AWS)         SAS         R (programming language)         Tableau         PHP and Javascript Each of these technologies will help you with a different sector. For example, you will need to use cloud solutions for data storage and access. On the other hand, you will need to use R for using data science tools. These are all the problems you need to face and fix when you work on big data project ideas.  If you are not familiar with any of the technologies we mentioned above, you should learn about the same before working on a project. The more big data project ideas you try, the more experience you gain. Otherwise, you’d be prone to making a lot of mistakes which you could’ve easily avoided. So, here are a few Big Data Project ideas which beginners can work on: Read: Career in big data and its scope. Big Data Project Ideas: Beginners Level This list of big data project ideas for students is suited for beginners, and those just starting out with big data. These big data project ideas will get you going with all the practicalities you need to succeed in your career as a big data developer. Further, if you’re looking for big data project ideas for final year, this list should get you going. So, without further ado, let’s jump straight into some big data project ideas with source code that will strengthen your base and allow you to climb up the ladder. We know how challenging it is to find the right project ideas as a beginner. You don’t know what you should be working on, and you don’t see how it will benefit you. That’s why we have prepared the following list of big data projects with source code so you can start working on them: Let’s start with big data project ideas. Fun Big Data Project Ideas Social Media Trend Analysis: Gather data from various platforms and analyze trends, topics, and sentiment. Music Recommender System: Build a personalized music recommendation engine based on user preferences. Video Game Analytics: Analyze gaming data to identify patterns and player behavior. Real-time Traffic Analysis: Use data to create visualizations and optimize traffic routes. Energy Consumption Optimization: Analyze energy usage data to propose energy-saving strategies. Predicting Box Office Success: Develop a model to predict movie success based on various factors. Food Recipe Recommendation: Recommend recipes based on dietary preferences and history. Wildlife Tracking and Conservation: Use big data to track and monitor wildlife for conservation efforts. Fashion Trend Analysis: Analyze fashion data to identify trends and popular styles. Online Gaming Community Analysis: Understand player behavior and social interactions in gaming communities. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript 1. Classify 1994 Census Income Data One of the best ideas to start experimenting you hands-on big data projects for students is working on this project. You will have to build a model to predict if the income of an individual in the US is more or less than $50,000 based on the data available. A person’s income depends on a lot of factors, and you’ll have to take into account every one of them. Source Code: Classify 1994 Census Income Data 2. Analyze Crime Rates in Chicago Law enforcement agencies take the help of big data to find patterns in the crimes taking place. Doing this helps the agencies in predicting future events and helps them in mitigating the crime rates. You will have to find patterns, create models, and then validate your model. Source Code: Analyze Crime Rates in Chicago 3. Text Mining Project This is one of the excellent deep learning project ideas for beginners. Text mining is in high demand, and it will help you a lot in showcasing your strengths as a data scientist. In this project, you will have to perform text analysis and visualization of the provided documents.   You will have to use Natural Language Process Techniques for this task. Source Code: Text Mining Project In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Big Data Project Ideas: Advanced Level 4. Big Data for cybersecurity This project will investigate the long-term and time-invariant dependence relationships in large volumes of data. The main aim of this Big Data project is to combat real-world cybersecurity problems by exploiting vulnerability disclosure trends with complex multivariate time series data. This cybersecurity project seeks to establish an innovative and robust statistical framework to help you gain an in-depth understanding of the disclosure dynamics and their intriguing dependence structures. Source Code: Big Data for cybersecurity 5. Health status prediction This is one of the interesting big data project ideas. This Big Data project is designed to predict the health status based on massive datasets. It will involve the creation of a machine learning model that can accurately classify users according to their health attributes to qualify them as having or not having heart diseases. Decision trees are the best machine learning method for classification, and hence, it is the ideal prediction tool for this project. The feature selection approach will help enhance the classification accuracy of the ML model. Source Code: Health status prediction 6. Anomaly detection in cloud servers In this project, an anomaly detection approach will be implemented for streaming large datasets. The proposed project will detect anomalies in cloud servers by leveraging two core algorithms – state summarization and novel nested-arc hidden semi-Markov model (NAHSMM). While state summarization will extract usage behaviour reflective states from raw sequences, NAHSMM will create an anomaly detection algorithm with a forensic module to obtain the normal behaviour threshold in the training phase. Source Code: Anomaly detection 7. Recruitment for Big Data job profiles Recruitment is a challenging job responsibility of the HR department of any company. Here, we’ll create a Big Data project that can analyze vast amounts of data gathered from real-world job posts published online. The project involves three steps: Identify four Big Data job families in the given dataset. Identify nine homogeneous groups of Big Data skills that are highly valued by companies.  Characterize each Big Data job family according to the level of competence required for each Big Data skill set. The goal of this project is to help the HR department find better recruitments for Big Data job roles. Source Code: Recruitment for Big Data job 8. Malicious user detection in Big Data collection This is one of the trending deep learning project ideas. When talking about Big Data collections, the trustworthiness (reliability) of users is of supreme importance. In this project, we will calculate the reliability factor of users in a given Big Data collection. To achieve this, the project will divide the trustworthiness into familiarity and similarity trustworthiness. Furthermore, it will divide all the participants into small groups according to the similarity trustworthiness factor and then calculate the trustworthiness of each group separately to reduce the computational complexity. This grouping strategy allows the project to represent the trust level of a particular group as a whole.  Source Code: Malicious user detection 9. Tourist behaviour analysis This is one of the excellent big data project ideas. This Big Data project is designed to analyze the tourist behaviour to identify tourists’ interests and most visited locations and accordingly, predict future tourism demands. The project involves four steps:  Textual metadata processing to extract a list of interest candidates from geotagged pictures.  Geographical data clustering to identify popular tourist locations for each of the identified tourist interests.  Representative photo identification for each tourist interest.  Time series modelling to construct a time series data by counting the number of tourists on a monthly basis.  Source Code: Tourist behaviour analysis 10. Credit Scoring This project seeks to explore the value of Big Data for credit scoring. The primary idea behind this project is to investigate the performance of both statistical and economic models. To do so, it will use a unique combination of datasets that contains call-detail records along with the credit and debit account information of customers for creating appropriate scorecards for credit card applicants. This will help to predict the creditworthiness of credit card applicants. Source Code: Credit Scoring 11. Electricity price forecasting This is one of the interesting big data project ideas. This project is explicitly designed to forecast electricity prices by leveraging Big Data sets. The model exploits the SVM classifier to predict the electricity price. However, during the training phase in SVM classification, the model will include even the irrelevant and redundant features which reduce its forecasting accuracy. To address this problem, we will use two methods – Grey Correlation Analysis (GCA) and Principle Component Analysis. These methods help select important features while eliminating all the unnecessary elements, thereby improving the classification accuracy of the model. Source Code: Electricity price forecasting 12. BusBeat BusBeat is an early event detection system that utilizes GPS trajectories of periodic-cars travelling routinely in an urban area. This project proposes data interpolation and the network-based event detection techniques to implement early event detection with GPS trajectory data successfully. The data interpolation technique helps to recover missing values in the GPS data using the primary feature of the periodic-cars, and the network analysis estimates an event venue location. Source Code: BusBeat 13. Yandex.Traffic Yandex.Traffic was born when Yandex decided to use its advanced data analysis skills to develop an app that can analyze information collected from multiple sources and display a real-time map of traffic conditions in a city. After collecting large volumes of data from disparate sources, Yandex.Traffic analyses the data to map accurate results on a particular city’s map via Yandex.Maps, Yandex’s web-based mapping service. Not just that, Yandex.Traffic can also calculate the average level of congestion on a scale of 0 to 10 for large cities with serious traffic jam issues. Yandex.Traffic sources information directly from those who create traffic to paint an accurate picture of traffic congestion in a city, thereby allowing drivers to help one another. Source Code: Yandex.Traffic Additional Topics         Predicting effective missing data by using Multivariable Time Series on Apache Spark         Confidentially preserving big data paradigm and detecting collaborative spam         Predict mixed type multi-outcome by using the paradigm in healthcare application         Use an innovative MapReduce mechanism and scale Big HDT Semantic Data Compression         Model medical texts for Distributed Representation (Skip Gram Approach based) Learn: Mapreduce in big data More Fun Big Data Projects Some more exciting big data projects to develop your skills include: Traffic Control Using Big Data Traffic issues are a common burden for many major cities, especially during peak hours. To address this problem, regularly monitoring popular and alternate routes for traffic may provide some relief. Leveraging the power of big data projects with real-time traffic simulation and predictions offers numerous advantages. In fact, this cutting-edge technology has already demonstrated success in effectively modeling traffic patterns. Take, for example, the Lambda Architecture program designed to tackle traffic challenges in Chicago. By tracking over 1,250 city roads, this program provides up-to-date information on traffic flow and traffic violations. Source Code: Traffic Control Using Big Data Search Engines Search engines manage trillions of network objects and track online user movements to decode their search requests. But how do search engines make sense of all this information? They do so by transforming the vast amount of website content into measurable data. This presents an exciting opportunity for curious newbies looking to delve into the world of big data projects and Hadoop. Specifically, they can hone their skills in querying and analyzing data with the help of Apache Hive. With its SQL-like interface, Hive offers a user-friendly way to access data from a variety of Hadoop-based databases. Anyone already familiar with SQL will find this project easy to complete. Source Code: Search Engines Medical Insurance Fraud Detection Medical insurance fraud detection is quite easy with cutting-edge data science methodologies. By leveraging real-time analysis and advanced classification algorithms, this approach can promote trust in the medical insurance industry. It is one of the big data projects that address the issue of healthcare costs alongside preventing fraud. This project harnesses the power of data analytics to uncover critical links between healthcare professionals. Source Code: Medical Insurance Fraud Detection Data Warehouse Design If you are interested in big data projects related to e-commerce sites, this one is recommended for you. Your task will be to construct a data warehouse for a retail enterprise. This project has a particular focus on optimizing pricing and inventory allocation. This project will help identify whether certain markets have an inclination toward high-priced products. Moreover, it will help you understand whether price adjustment or inventory redistribution is necessary according to locations. Get ready to harness the power of big data to uncover valuable insights in these areas. Source Code: Data Warehouse Design Recommendation System The vast world of online services offers access to an endless array of items. You will find music, video clips, and more. Big data can help create recommendation systems that will provide you with tailored suggestions. All big data projects analyze user data to effectively offer recommendations. They will consider browsing history and other metrics to come up with the right suggestions. In this specific big data project, you will have to leverage different recommendation models available on the Hadoop Framework. This will ensure that you understand which model will deliver optimal outcomes. Source Code: Recommendation System Wikipedia Trend Visualization Human brains get exposed to different formats of data. But our brains are programmed to understand visual data better than anything else. In fact, the brain can comprehend visual data within only 13 milliseconds. Wikipedia is a go-to destination for a vast number of individuals all over the world for research purposes or general knowledge. At times, people visit these pages out of pure curiosity. The endless amount of data within its pages can be harnessed and refined through the use of Hadoop. By utilizing Zeppelin notebooks, this data can then be transformed into visually appealing insights. This will enable a deeper understanding of trends and patterns across different demographics and parameters. Therefore, it is one of the best big data projects to understand the potential of visualization. Source Code: Wikipedia Trend Visualization Website Clickstream Data Visualization Clickstream data analysis is about understanding the web pages visited by a specific user. This type of analysis helps with web page marketing and product management. Moreover, clickstream data analysis can help with creating targeted advertisements. Users will always visit websites according to their interests and needs. So, clickstream analysis is all about figuring out what a user is on the lookout for. It is one of the big data projects that need the Hadoop framework. Source Code: Clickstream data analysis Image Caption Generation The growing influence of social media requires businesses to produce engaging content. Catchy images are definitely important on social media profiles. But businesses also need to add attractive captions to describe the images. With captions and useful hashtags, businesses are able to reach the intended target audience more easily. Producing relevant captions for images requires dealing with large datasets. Therefore, image caption generation can be one of the most interesting big data projects. This project involves processing images with the help of deep learning techniques. It helps in understanding the image and creating appealing captions with AI. Python is often the source code behind these big data projects. So, it is better to proceed with this project after working on something with Python as the source code. Source Code: Image Caption Generation GIS Analytics for Effective Waste Management Large amounts of waste pose a threat to the environment and our well-being. Proper waste management is necessary for addressing this issue. Waste management is not just about collecting unwanted items and their disposal. It also involves the transportation and recycling of waste. Waste management can be one of the most interesting big data projects by leveraging the power of GIS modeling. These models can help create a strategic path for collecting waste. Moreover, data experts can create routes to dispose of waste at designated areas like landfills or recycling centers. Additionally, these big data projects can help find ideal locations for landfills. These projects can also help with the proper placement of garbage bins all over the city. Source Code: Waste Management Network Traffic and Call Data Analysis The telecommunication industry produces heaps of data every day. But only a small amount of this data can be useful for improving business practices. The real challenge is in dealing with such vast volumes of data in real time. One of the most interesting big data projects is analyzing the data available in the telecommunications sector. It will help the telecom industry to undertake decisions regarding the improvement of customer experience. This big data project will involve analyzing the network traffic. As a result, it will become easier to address issues like network interruptions and call drops. By assessing the usage patterns of customers, telecom companies will be able to create better service plans. As a result, customers will be satisfied with plans that fulfill their overall needs. The tools used for this kind of big data project will depend on its complexity. Source Code: Network Traffic Fruit Image Classification This can be one of the most interesting big data projects with professionals working on a mobile application. It will be a mobile app capable of providing insights about fruit harvesting by analyzing different pictures. This project will involve leveraging AWS cloud tools to develop a data processing chain. Some steps in this chain will include dimensionality reduction and operating a fruit image classification engine. While working on this big data project, you will have to generate PySpark scripts. Your task will become easier with a big data architecture created on an EC2 Linux server. Due to its compatibility with AWS, DataBricks is also ideal for this project. Source Code: Fruit Image Classification Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Conclusion In this article, we have covered top big data project ideas. We started with some beginner projects which you can solve with ease. Once you finish with these simple projects, I suggest you go back, learn a few more concepts and then try the intermediate projects. When you feel confident, you can then tackle the advanced projects. If you wish to improve your big data skills, you need to get your hands on these big data project ideas. Working on big data projects will help you find your strong and weak points. Completing these projects will give you real-life experience of working as a data scientist. If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.

by Mukesh Kumar

29 May 2024

Blogs

908094

Wondering what is the range of DevOps Salary in India? In the last few years, DevOps has evolved from being just a “buzzword” to a mainstream practice in the IT world. According to Statista, as more and more organizations are adopting DevOps, there has been a 17% increase in 2018 from only 10% in 2017.  Source  Furthermore, the 2019 State of DevOps Report by Puppet Labs shows a positive trend in the global adoption of DevOps. Because of this reason, companies are not shying away from offering increased DevOps salary in India. Check out our free courses related to software development. Source Thus, if you have the required skillset and are ready to keep yourself updated, your career as a DevOps Engineer is expected to keep growing onwards and upwards. This line stands true especially when we consider that a DevOps salary in India is directly or indirectly dependent on how upskilled and updated they are. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Check out our best devops courses at upGrad Our Learners also read: Devops career path! This, in turn, has spurred the demand for skilled DevOps professionals, who are among the highest-paid IT professionals today. Tech giants like Oracle, Barclays, Accenture, Tech Mahindra, TCS, IBM, Wipro, Facebook, to name a few, are always seeking professionals with DevOps skills. Even e-commerce companies like Target, Amazon, eBay, etc. are readily adopting DevOps, thereby creating plenty of employment opportunities for DevOps professionals. The top roles in DevOps include DevOps Architect, DevOps Engineer, DevOps manager, Security Engineer, Automation Engineer, and Release manager. The demand for these engineers are always high. The list is endless. That’s the primary reason DevOps salary in India is quite high. The ever-increasing demand. Check out our Advanced Certification in DevOps We are sure, this must-have sparked a will to become a DevOps Engineer within you! Let’s take a look at who exactly is a DevOps Engineer and what is a typical DevOps salary in India? One-Of-Its-Kind Program That Creates Skilled Software Developers. Apply Now! Get Software Engineering degrees from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. What is DevOps? DevOps is nothing but a set of cultural practices, philosophies, and tools that help n organization to deliver applications and services faster than usual and improve and evolve their products at a much brisker pace than software development organizations that are still stuck with traditional infrastructure management processes. This ability to work at speed allows organizations to develop a much-needed competitive edge and at the same time, serve their customers in a better way. It is a term that has emerged out of the coming together of two related concepts. One of these concepts is agile operations or agile infrastructure. Agile operation is a concept that involves the application of Lean and Agile methodologies to operations. The second concept is a much broader one and involves a thorough understanding of how collaboration between development and operations teams through the software development lifecycle brings value. Because of the demand, DevOps salary in India is one of the highest. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses upGrad’s Exclusive Software and Tech Webinar for you – SAAS Business – What is So Different? document.createElement('video'); https://cdn.upgrad.com/blog/mausmi-ambastha.mp4   What does a DevOps Engineer do? DevOps Engineers work in close collaboration with Software Developers, System Operators (SysOps), and other production IT members to manage and supervise code releases. They must be well-versed in IT infrastructure management that is integral for supporting the software code in dedicated, multi-tenant, or hybrid cloud environments. Our learners also read: Java free online courses! In a DevOps model, the development and operations teams do not function separately as ‘siloed’ units but merge together. Also, this approach to software development demands frequent and incremental changes. Hence, DevOps Engineers have to perform a wide range of functions across the entire application lifecycle – from development and test to deployment and operations. This calls for a versatile skill set that is not limited to a particular function or role. Check out our Advanced Certification in Blockchain To successfully implement the DevOps approach, DevOps Engineers must be well-versed in the best practices of DevOps methodology, which include: Continuous Integration – This practice requires developers to merge the alterations in their code into a central repository, after which it runs the automated builds and tests. Continuous Delivery – In this practice, the code changes are built, tested, and prepared automatically for the production release. Continuous deployment (CD) –This practice is about automatically submitting the changes done by a developer from the repository to the production process. It, therefore, saves valuable time because the operations team need not do it themselves. Infrastructure as Code – This practice encourages the provision and management of the infrastructure using specific code and software development techniques (version control, continuous integration, etc.). Monitoring and Logging – Monitoring and logging are essential to check and measure the metrics of applications and infrastructure and see how their performance affects the user experience of a product/service. With suitable monitoring, you can rapidly troubleshoot problems with the help of root cause analysis that identifies where a problem started. The AWS devops engineer salary because these engineers are skilled at monitoring and logging processes. Monitoring helps them identify how various systems influence each other –when they operate sequentially and simultaneously. A DevOps engineer’s job is nearly impossible without an exhaustive monitoring solution. Conversely, if a DevOps engineer can monitor all elements of your stack, the time-consuming and major issues can be quickly resolved. So, the ability to develop a quick solution suggests a decent AWS devops engineer salary. Communication and Collaboration – DevOps encourages increased communication and collaboration within organizations. Microservices Architecture – It is a design approach used to develop a single application as a component of small services. So, there are opportunities galore for DevOps developers, and the indispensable nature of their jobs ensures that DevOps salary in India is quite elevated. Container orchestration -DevOps engineers use container orchestration to ensure processes execute automatically and more efficiently. It also helps them to ascertain that designing rules dictate the conditions under which containers are shut down or spun up. Commonly, containerization is used to stabilize the use of resources as per the existing workload.  You can earn an admirable AWS devops engineer salary in India after learning how to build containers, Java, Go, Node.js, and Python 3. Now, we move on to the main functions and responsibilities of a DevOps Engineer. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Responsibilities of DevOps Developer To make your career to grow as a DevOps developer and earn the highest possible DevOps salary in India, you need to have been familiar with these responsibilities: 1.Project Planning DevOps Engineers are an integral part of the project planning operation. Their skills in software development and system options, and business expertise (the risk, impact, and costs vs. benefits) allows them to foresee the project needs and resources. 2. Product Development DevOps Engineers are responsible for developing, building, and managing IT solutions. To meet this end, they have to install and configure solutions, implement reusable components, translate technical requirements, perform script maintenance and updates, assist operations teams at all phases of data testing, develop interface stubs and simulators, to name a few. 3. Product Deployment DevOps Engineers design and develop automated deployment arrangements by leveraging configuration management technology. This allows them to deploy new modules/upgrades and fixes in the production environment itself. 4. Performance Management Apart from evaluating existing applications and platforms, DevOps Engineers also offer recommendations for enhancing the performance. 5. Maintenance and Troubleshooting Maintenance and troubleshooting are two routine tasks of DevOps Engineers. Using strategy-building techniques, they delineate the requirements and procedures for implementing regular maintenance. 6. Security Integration DevOps developers bear the crucial responsibility of integrating security practices flawlessly into every stage of software development and hold high devops salary in India. This pivotal role includes incorporating stringent security measures from code inception to deployment phase, thereby empowering developers to identify and resolve vulnerabilities in early developmental phases – a process that ensures alignment with rigorous end-product security standards. 7. Capacity Planning and Scalability DevOps Developers analyze system performance and ensure that the infrastructure can scale to meet increasing demands, with decent devops engineering salary in India. Through diligent capacity planning, they anticipate resource requirements proactively. With the given devops engineer salary, they fine-tune the system for optimal efficiency during peak periods, a process which underlines their pivotal role in crafting systems – ones capable of expanding effortlessly in response to evolving user demands. This approach accentuates scalability’s crucial aspect and managing heightened workloads becomes more manageable as a result, in such devops salary, especially, devops salary India. 8. Disaster Recovery Planning DevOps Developers significantly contribute to creating and testing disaster recovery plans. These strategic blueprints guarantee the swift restoration of systems and data in unforeseen outages or catastrophes. Through consistent plan development coupled with rigorous testing, DevOps Developers play a vital role in minimizing downtime for organizations. Moreover, they secure continuity of critical business operations, an invaluable asset for any company,  with such devops fresher salary in India. 9. Cost Optimization Optimizing resource utilization to minimize infrastructure costs falls under the responsibility of DevOps Developers,  especially in such devops fresher salary. They must monitor and analyze resource consumption, identify areas of inefficiency, and implement strategies to reduce operational expenses. The organization’s financial sustainability benefits when DevOps Developers adopt cost-effective solutions, mostly with bearing average salary of devops engineer in India, or aws devops salary in India. 10. Automation of Business Processes DevOps Developers not only automate the software development lifecycle, but they also extend this automation to business processes. Their efforts involve automating repetitive tasks and workflows as well as streamlining overall operations through reducing manual interventions thus minimizing human error. By enhancing efficiency with automation, they accelerate time-to-market and guarantee consistency in the delivery process. 11. Knowledge Sharing and Training Fostering a culture of continuous learning and knowledge sharing within the organization is the vital role DevOps Developers play,  including cloud devops engineer salary, and senior devops engineer salary. Their responsibilities include conducting training sessions, creating documentation notably for best practices in DevOps principles and tools, a task paramount to ensuring team-wide proficiency. By facilitating this transfer of expertise, teams remain aligned, enhancing collaboration and hastening their adoption of new technologies thus reaping significant benefits from shared knowledge. DevOps Developers deeply involve themselves in project planning, development, and deployment among other well-established responsibilities. Their contributions also span critical areas of security integration, capacity planning, disaster recovery planning and cost optimization – all key elements for organizational success. Furthermore, automation of business processes and knowledge sharing not only drive innovation but foster efficiency and collaboration within the workplace. It’s a dual-purpose approach that distinguishes proficient DevOps developers from their peers, like aws devops  salary, and azure devops engineer salary. Read: Career in DevOps DevOps RoadMap Understanding the working of Cloud Service Platforms: Modern IT infrastructure often builds its foundation on cloud service platforms. A DevOps practitioner, further expanding into this domain, must explore specific cloud providers such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). With a comprehensive understanding of these platforms’ nuances, their services, deployment models and pricing structures, DevOps engineers can optimize infrastructure as they harness the full potential of solutions based in the clouds. Furthermore, it is crucial to master Infrastructure as Code (IaC) tools such as Terraform and CloudFormation. The codification of infrastructure through IaC promotes consistency and scalability. Therefore, proficiency in these tools becomes indispensable. Aligning with the DevOps ethos of automation and reproducibility involves learning how to design, provision, and manage infrastructure via code. One should learn the working of Cloud service platforms because many companies are using their infrastructure on the cloud. It is an essential skill which should be learned. Knowledge of Using Tool to Monitor and Collection of Important Feedback: Continuous improvement drives DevOps, with its core being effective monitoring. Although valuable tools such as Splunk and Nagios exist, a deeper understanding of system performance, resource utilization, and application behavior can emerge by delving into advanced solutions like Prometheus and Grafana. DevOps engineers, in their pursuit of proactive issue identification and resolution, must grasp the setup of meaningful alerts, dashboards, and logs. They should also integrate with incident response systems like PagerDuty or OpsGenie for instance to guarantee a swift reaction to potential disruptions. This is crucial. Further exploration into Application Performance Monitoring (APM) tools such as New Relic or Datadog is recommended. It grants them visibility into application-level performance. Implementation of changes as soon as possible after the collection of feedback is an essential skill for a DevOps Engineer. Knowledge of Monitoring tools such as Splunk, Nagios and many more are necessary for a DevOps Engineer to learn. Knowledge and Experience of Deployment of Application: In ensuring smooth and reliable application delivery, pivotal roles are played by deployment strategies. Docker and Puppet serve as essential tools. However, extending the knowledge to Kubernetes for container orchestration enhances the ability with a dash of efficiency to manage complex applications based on microservices effectively. Embracing practices such as Infrastructure as Code which defines and automates deployment pipelines using tools like Jenkins, GitLab CI, or CircleCI, we foster consistency and repeatability. Further automation through Continuous Deployment (CD) minimizes the release process’s manual interventions. A DevOps Engineer should know how to deploy in the server of production. Knowledge of Configuration and Deployment in the production environment by using tools such as Docker and Puppet is beneficial. Knowledge of Continuous Testing, Continuous Delivery, and Continuous Integration Adopting a holistic approach to testing and seamlessly integrating it into the development pipeline is the imperative of every DevOps engineer. In addition to automated unit tests, a baseline requirement, considering Behavior-Driven Development (BDD) frameworks such as Cucumber or Behave not only enhances collaboration between QA teams but also fosters an environment where developers are more aligned with quality assurance objectives. By implementing Continuous Integration (CI) using tools such as Jenkins or GitLab CI, we ensure regular integration, testing and validation of code changes. When coupled with Continuous Delivery (CD), this process automates the deployment of tested code into production-like environments thus fostering a culture that promotes frequent and incremental releases. DevOps need to know how things can be automated in the testing stage. The code should be less complicated so that it can run fine in both development and testing environments. Good Knowledge of Tool for Source Code Management and Build Tools: DevOps engineers should extend their understanding of version control systems beyond Git. They ought to delve into advanced Git workflows like Gitflow or GitHub Flow. Proficiency in managing branches effectively, resolving conflicts and preserving a clean commit history enhances the efficiency of development processes. Gradle and Maven, two standout build tools in their field, offer potent capabilities for dependency management and project automation. When we integrate these tools into the CI/CD pipeline, a procedure that emphasizes continuous integration and delivery of software updates, we secure an efficient, reproducible and scalable build process. A competent DevOps Engineer should know how to process the building of automation. One should know how to build the commit continuously. There are some automation servers that DevOps should know, such as the working of Jenkins. Skills of Source Code Management: Foundational proficiency in Git remains essential. However, a DevOps engineer must also delve into the advanced features of Git. This involves grasping concepts such as rebasing and cherry-picking, along with submodule management. These provide a more nuanced approach to codebase control. Furthermore, by exploring Git hooks, it’s possible to automate both pre- and post-commit tasks, consequently enhancing the overall development workflow. A deep understanding of GitLab or GitHub Actions for automating workflows is essential encompassing the critical processes of testing, building and deploying applications. By harnessing capabilities inherent within these platforms one can realize a development lifecycle that’s not only more integrated but also vastly automated. A DevOps Engineer should have proficiency in at least one source code management tool (version control system) such as Git. Average DevOps Salary in India The demand for DevOps is high but companies require individuals to have the correct skill sets. Additionally, the better the experience, the higher is the salary. The average devops salary in India, according to Payscale, is Rs 674,202 per year, inclusive of bonuses and profit-sharing. Source City Average Annual Salary Bangalore ₹8.4 Lakhs Mumbai ₹8.3 Lakhs Hyderabad ₹8.2 Lakhs Pune ₹8.5 Lakhs Kolkata ₹7.6 Lakhs Chennai ₹8.3 Lakhs Source Factors Affecting DevOps Salary in India The four main factors affecting the DevOps Salary in India are: Company – The company that you work for will have a direct impact on the salary you get. Experience – The more experience you have, the better is ability to understand the roadblocks and provide quick solutions for bugs. A combination of experience and company will get salaries and positions that help create a mark. Location – In the coming years, location is all set to play a smaller role in the salary you can expect. But, even then, location will continue to have an impact on the salary you can ask for and get from a prospective employer. Skill – The important skills you have, the better is the ability to understand. Location is all set to play a vital role in deciding the DevOps engineer salary in India. DevOps Salary in India: Based On Company It is an accepted fact, the bigger the brand or company, the higher is the salary an individual can expect. Let us look at some of the highest pay packages – Tata Consultancy Services Limited offers about Rs. 6,27,000 per annum as salary. It is followed by Accenture Technology Solutions at Rs 5,63,000. DevOps engineer salary Chennai and other cities depend on the employer. Source Companies Average Annual Salary Forbes Global 2000 ₹8.6 Lakhs Public ₹8.3 Lakhs Fortune India 500 ₹7.8 Lakhs Conglomerate ₹8.5 Lakhs Startup ₹8 Lakhs MNC ₹11.4 Lakhs Source DevOps Salary in India: Based on Experience The more experience you have the higher your pay package will be. So, if you are a beginner and have only recently entered the industry, you will have to moderate your hopes accordingly. So, let us break down the DevOps engineer salary in India, depending on how many years of experience you have in your bag – Let’s see how a DevOps engineer salary in India varies based on experience. Source Entry-Level Engineer Salary in India The average entry-level DevOps salary in India is ₹366,337. Source Mid-Level DevOps Salary in India The average mid-level DevOps salary in India is ₹1,142,206. Source Experienced DevOps Salary in India The average experienced DevOps salary in India is ₹1,632,155. Source Let’s see how the DevOps engineer in India increases with experience: Source Years Experience Average Annual Salary 2 Years ₹5.6 Lakhs 3 Years ₹6.6 Lakhs 4 Years ₹7.9 Lakhs 5 Years ₹9.7 Lakhs 6 Years ₹11.4 Lakhs Source DevOps Salary in India: Based on Location Though Covid-19 may reduce the impact of location on salaries in the future, currently, location plays a crucial factor in determining DevOps salary in India. The Silicon Valley of India, Gurgaon, Haryana offers the highest salaries, with DevOps engineers in the city earning approximately 20% more than their counterparts in other cities. The comparison is done against the national average. Chennai, Tamil Nadu the other city competing for the Silicon Valley title, offers salaries that are 9% less than the national average. So, you can expect admirable DevOps engineer salary Chennai and DevOps engineer salary Mumbai. Source City Average Annual Salary Bangalore ₹8.4 Lakhs Mumbai ₹8.3 Lakhs Hyderabad ₹8.2 Lakhs Pune ₹8.5 Lakhs Kolkata ₹7.6 Lakhs Chennai ₹8.3 Lakhs Source DevOps Salary in India: Based on Skills Let’s discuss how DevOps engineer salary in India increases with the skillset. As we mentioned earlier, you have to possess an impressive set of skills to acquire the best-paid jobs in the highly competitive market for DevOps engineers. Some skills get you a higher paycheque. So, try to invest in these skills. Development Operations(DevOps) – ₹694,145 Amazon Web Services(AWS) – ₹683,831 Linux – ₹671,250 Jenkins – ₹692,795 Python- ₹723,891 Source Development Operation and Amazon Web Services are sure to bring you more than the average DevOps engineer salary in India. In terms of popularity response too, these two skills ranked the highest. DevOps Salary in Other Countries DevOps engineer salary Mumbai and other Indian cities are admirable but let’s now look at the DevOps salary in other countries. DevOps salary in The US is $94,366. Source DevOps salary is The UK is £41,855. Source Most Demanding DevOps Skills 1. Linux Fundamentals And Scripting Gone are those days when companies worried about proprietary operating systems. We are now in the era of Linux. Most companies prefer hosting their applications on Linux. As such, skills in handling a Linux operating system are very crucial for a DevOps Engineer. So, a DevOps Engineer must be able to handle any scripting language and must be well-versed with at least one scripting language (preferably Python). So the inevitable DevOps skillset would be Linux and Linux based scripting. 2. Knowledge On Various DevOps Tools And Technologies DevOps practices are executed in various stages, and each stage has tools that can facilitate that stage. The main stages are Source Code Management Developers create and modify the software codes regular. Source code management software is used to store the code, merge new code to the old one, control the code versions, etc.  Configuration Management Configuration management is used to keep track of software and operating system configurations. It also helps to manage configurations across thousands of servers. It is done through a click of a button or the execution of a single command on the management server. Continuous Integration Continuous integration, abbreviated as CI, is the process of automating the code integrations from different developers into a single piece of software. Continuous Testing Continuous testing is the process of reducing feedback waiting time after code testings. The code is tested in the development environment itself using automation testing tools. Continuous Monitoring Continuous monitoring is used to monitor application performance, downtimes, error logs, etc. The main tools used are Nagios, Zabbix, Splunk, etc. Containerization Containerization is the process of virtualizing an operating system so that different containers running different applications can share the operating system and system resources. Containerization allows isolating processes from the other part of the software. Every container works as a virtual machine that runs a piece of the entire process. Many organizations evaluate the knowledge of containerization when they evaluate AWS devops salary. Since containers can be shut down and spun up very quickly, containerization makes it simpler and cost-effective to use various aspects of the software. A DevOps engineer must understand containerization because it influences how a product is developed, tailored, and tested. DevOps engineers skilled in these aspects can earn admirable AWS devops salary. 3. Continuous Integration And Continuous Delivery Continuous Integration And Continuous Delivery or abbreviated as CI/CD is the essence of DevOps. It is the practice of continuously integrating all the code from different developers, continuously testing them, and deploying the successful code to production. 4. Infrastructure As Code Previously provisioning IT infrastructure was a long, tedious, and manual process. With the birth of cloud computing and containerization technologies, the infrastructure needed by developers can be provisioned through an automated script. 5. Key concepts of DevOps  DevOps unlike what people think, is neither a technology nor a tool. It’s a methodology that doesn’t have a strict framework. So companies can adopt the methodology by tailoring the framework as per their standards. 6. Soft skills Soft skills play a great role in the IT industry with the adoption of DevOps methodology. Most companies prefer an Agile way of working using DevOps methodologies so that teams work in high velocity and produce deliverable faster to meet client expectations.  How does a DevOps Engineer enhance the DevOps culture? People consider DevOps more inclined towards a cultural shift rather than an innovative development method. DevOps engineers enhance a culture of collaboration, communication, and shared responsibility between all parties for the whole development lifecycle. One of the greatest challenges a DevOps engineer faces is letting all stakeholders work together. The stakeholders include front-end developers, back-end developers, UI/UX developers, product testers, IT operations admins, security admins, executives, and customer service Reps who deal with end-users. All these stakeholders are equally significant in the DevOps culture. Their respective visions share the same value.  The AWS devops engineer salary in India is increasing because these engineers are expected to bring a cultural shift. This shift may need developers and executives to improve their listening skills. A DevOps Engineer may need to guide them through the cultural shift process. 1. Promoting Collaboration The DevOps salary in India hinges on the ability to merge the operations and development teams. Essential is the encouragement of open communication and collaboration, traditionally limited in these departments. This undertaking, organizing cross-functional team meetings, fostering an environment where knowledge is shared freely, requires not just technical proficiency but also promoting empathy within developers,testers, and personnel from operations. A DevOps Engineer dismantles communication barriers, thereby fostering a work environment characterized by collaboration and cohesiveness. 2. Emphasizing Continuous Learning DevOps, a field perpetually in evolution, witnesses regular emergence of new tools and practices. A culture rooted in continuous learning, an environment the DevOps Engineer must foster within their team, requires not only organization of training sessions but also sharing industry trends along with best practices. Most importantly, it needs nurturing an experimental mindset that accepts failures as opportunities for growth and learning. A DevOps Engineer fosters an environment of adaptability and innovation by actively staying current with the latest developments, sharing knowledge, and promoting a culture that values continuous learning. 3. Establishing a blame-free Culture The software development process inherently incorporates failure. In emphasizing a blame-free approach to failures, a DevOps Engineer contributes positively to the culture. Rather than attributing issues with blame, we should focus on identifying root causes, learning from mistakes. A proactive attitude towards problem-solving receives encouragement from this culture shift. Moreover, it nurtures an environment in which team members feel secure to take risks and innovate. 4. Implementing DevOps Metrics Subjective feelings of collaboration do not solely define DevOps. We can quantify and measure it. A DevOps Engineer has the ability to introduce key performance indicators (KPIs) that gauge several aspects, including the development pipeline’s efficiency, deployment frequency, lead time and even mean time for recovery. This data-driven approach enhances transparency and accountability, contributing to a culture of continuous improvement. 5. Encouraging Cross-Functional Skills To obtain a good DevOps salary in India a DevOps Engineer ought to champion cross-functional skill development within the team. This entails fostering an environment where developers comprehend operations and vice versa. Through cultivating a culture that liberates team members from confinement in their specific roles, thereby promoting holistic understanding of the entire software delivery process, a versatile and adaptable team emerges. How to Become a DevOps Engineer? You must obtain the experience and knowledge required to work with different technologies and teams if you want to become a DevOps engineer. It is crucial to learn the skills, implement them, and develop a portfolio that would impress potential employers to offer a praiseworthy DevOps engineer salary. It is better to work with different coding languages to understand how they are used to create solutions. Expanding your tech stack makes it simpler to troubleshoot issues and explain to stakeholders the opportunities and limitations. Another crucial aspect when it comes to earning a decent DevOps engineer salary is to become comfortable working with people from various backgrounds who specialize in different disciplines. Pursuing a relevant course can help in these cases. As you learn relevant skills, interact with various types of learners and tutors, and amass your portfolio, you will embark on your DevOps career with a nice Azure DevOps salary in India. What does it take to be a DevOps Engineer? At its core, the concept of DevOps seeks to combine and integrate the development and operations sides of software development into one continuous process. It encompasses the features of agile development, continuous integration, and continuous delivery, while also focusing on encouraging collaboration across the various IT departments (development, operations, support, and management teams). A DevOps Engineer is an IT professional with expertise in DevOps. These professionals have an extensive understanding of the software development life cycle (SLDC) and are adept in using a host of engineering tools and processes to solve complex operations challenges. Typically, DevOps Engineers have to juggle with different tasks, including coding, integrating, and testing – all aimed to enhance collaboration between teams and boost the development process.  As for the educational requirements, a DevOps Engineer must at least have a bachelor’s degree in Computer Science/Information Technology or any other related discipline. Apart from this, a strong foundational knowledge of data center migrations is beneficial. Other skills that a DevOps Engineer should possess are – SQL, Linux, Unix, MySQL, Python, Java, MongoDB, Puppet, Maven, Crucible, Jenkins, Software Configuration Management, and Cloud services (AWS/ Rackspace, Docker, OpenShift, etc.).  While the job role of a DevOps Engineer usually demands around 2-5 years of work experience in DevOps platforms, senior-level positions like that of a DevOps Architect/Manager, require at least 10-12 years of DevOps experience.  Some of the most in-demand skills of a DevOps Engineer are: Excellent technical understanding of cloud infrastructure services like Azure, AWS, etc. In-depth knowledge and working experience in compute virtualization, automation, and DevOps methodologies. Proficiency in environment management (Linux/Unix, Windows, Shell Scripting, Ansible, Chef, Puppet, etc.) Proficiency in coding and experience in Software-Defined Data Centers maintenance. Proficiency in writing and maintaining deployment scripts. Expertise in managing nodes and diagnosing application services and server/ network issues. DevOps Roles and Responsibilities  DevOps philosophy is fast evolving and new designations, roles, and responsibilities and emerging. An interesting fact is that though companies list these roles separately, there is a lot of overlap on the responsibilities, duties, and skill requirements. As of today, most companies have the below list of job titles for DevOps engineers DevOps Architect A DevOps architect is responsible for analyzing and executing DevOps practices within the organization or a team. He architects the overall DevOps environment bringing in all efficient DevOps methodologies as per industry standards. He brings in the right tools for automation of the processes. Automation Architect The Automation Architect is a crucial role in modern DevOps. A large number of DevOps depend on automated processes and workflows that streamline teams and reduce the need for intervention. So, the Automation Architect is one of the critical DevOps roles that can offer a great Azure devops salary in India. Candidates skilled with the Automation Architect position possess a profound familiarity with DevOps platforms as well as integrations. They can quickly troubleshoot and solve problems. Release Manager In a DevOps environment, a release manager is responsible for planning, scheduling, monitoring, and controlling the software development and deployment process. He develops the development team and the operations team to be in sync, enabling frequent but short feedback loops. From a DevOps perspective, the Release Manager is a significant management and oversight position that demands a profound level of practical development knowledge. Leadership is the key priority of this role. They are not only tasked with coordinating multiple releases and comprehending CI/CD but also need to map development pipeline strategies and coordinate those amongst teams. Security Engineer In the DevOps, world security is popularly known as DevSecOps. The responsibilities include using different tools like log management and configuration management to ensure security throughout. DevOps emphasizes on a continuous security philosophy. Security is a crucial aspect of the development process in DevOps. Security must be parched into the product from the ground to make sure it is harder to crack. This can be achieved by a security engineer who can closely work with the software developers. The DevOps engineer salary in India for 2 years experience can be high if they are skilled at ensuring system security. Security Engineers are important not only for a product’s success but also to guarantee safe operation.  These DevOps engineers must be fluent with contemporary security standards, continuing attack vectors, and user behaviours that commonly bypass security and create holes. Automation Engineer In the DevOps world, an automation engineer is responsible for developing and maintaining the CI/CD process for all the applications and their builds using tools like Maven, Jenkins, Git, Gerrit, Gradle, etc. He would be automating the configuration management across the infrastructure and platforms using configuration management tools like Ansible, Chef, Puppet, SaltStack, Fabric, etc. Software Tester A test engineer in the DevOps culture has to align their test design, test cases, and test automation with the DevOps framework being used. A software tester verifies that the code changes work as intended and also makes sure that the changes do not break the software product as a whole. Integration Specialist An integration specialist works with the infrastructure operations teams and software development teams to create the integration and continuity of the code. He creates a holistic view of the working environments. He supports engineering and testing teams to meet infrastructure needs. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? DevOps or Platform Engineer: If the organization is adopting a DevOps approach, it usually appoints a Platform Engineer to support the platform’s infrastructure. This infrastructure includes different managed services and in-house development. A DevOps or Platform engineer can get a decent DevOps engineer salary in India for 2 years experience if they can effectively support the platform’s infrastructure.  Site Reliability Engineer: Also known as Reliability Engineer, a Site Reliability Engineer is accountable for guaranteeing the quality of orchestration and integration of tools required to support daily operations. When people think about DevOps initially, this is the ideal role that strikes minds. They skillfully patch the existing infrastructure with data storage infrastructures and cloud solutions. This job role is significant in all DevOps organizations because the inability to guarantee sound integration can lead to costly outages. Data Analyst: DevOps Data Analysts examine and derive data to fulfil development goals. They are an important member of the development team because they use user data and existing data to critically think about patterns that influence user experience and other design issues. Some of their responsibilities overlap with QA Analysts and UX designers. Product Manager: It is the only job position in this list that is chiefly an operations position. Being operations team members, the product managers are tasked with collaborating with the dev team to ascertain that the product’s requirements are fulfilled to a high-quality standard. The ability to ensure product requirements are met can increase the chances of a decent DevOps engineer salary in India for 2 years experience as DevOps engineer. Top Companies Hiring DevOps Engineers in India Amazon With its extensive cloud services through Amazon Web Services (AWS), Amazon has consistently needed DevOps professionals to manage and optimize their cloud infrastructure. Google Businesses widely use Google Cloud Platform (GCP), and Google frequently looks for DevOps experts to enhance their cloud services and infrastructure. Microsoft Microsoft Azure is a leading cloud computing platform, and Microsoft often recruits DevOps engineers to work on Azure’s deployment, monitoring, and management solutions. Infosys As one of India’s largest IT services companies, Infosys frequently hires DevOps engineers to support its clients’ digital transformation initiatives, implementing DevOps best practices. Tata Consultancy Services (TCS) TCS is another major IT service player, actively seeking DevOps professionals to assist their clients in streamlining development and operations processes. Wipro Known for its IT services and consulting, focusing on digital transformation. DevOps engineers play a crucial role in their projects related to cloud computing and software development. Accenture Accenture, a global consulting and professional services company, often hires DevOps engineers to help clients accelerate their digital transformation journey through efficient software development and IT operations. IBM This company has a significant presence in India and continues to hire DevOps engineers to work on varied projects like, cloud computing, artificial intelligence, and data analytics. Paytm One of the leading multination company in India, Paytm invests in technology and frequently recruits DevOps experts to maintain their high-traffic platforms and ensure seamless operations. How to Obtain a Competitive DevOps Engineer Salary? Here are several key steps to help you obtain a competitive salary in the DevOps field: Master Core Skills Develop expertise in key DevOps tools and technologies like Docker, Kubernetes, Jenkins, Ansible, Git, and scripting languages like Python. Understanding the entire software development lifecycle is crucial. Continuous Learning DevOps is a rapidly evolving field. Stay updated with the latest trends, tools, and best practices. Regularly upskill through online courses, certifications, workshops, and attending industry conferences. Gain Practical Experience:  Apply your skills in real-world projects through internships, freelance work, or personal projects. Practical experience enhances your problem-solving abilities and makes you more valuable to employers. Collaboration and Communication DevOps is not just about technical skills; it also involves effective collaboration and communication between development and operations teams. Develop strong interpersonal skills and the ability to work well in a team. Certifications Consider earning certifications such as AWS Certified DevOps Engineer, Kubernetes Administrator, or Docker Certified Associate. Certifications validate your skills and can significantly boost your earning potential. Version Control and Collaboration Proficiency in version control systems (like Git) and collaboration platforms (like GitHub) is essential. Employers look for candidates who can efficiently manage code repositories and collaborate effectively with team members. Problem-Solving and Automation DevOps engineers should excel in problem-solving and be adept at automating repetitive tasks. Automating manual processes increases efficiency and productivity, making you invaluable to employers. Soft Skills Develop soft skills such as leadership, adaptability, and resilience. DevOps engineers often work in fast-paced environments where these skills are highly valued. Conclusion The opportunities for DevOps Developers are currently at their prime in India. We hope you liked our article on DevOps Developer’s salary in India. These numbers above are not set in stone. The real influencer of your salary is the skills you have,  the mastery you have attained over them, and how quickly you grow and make the company grow as well. By the end of this article, we hope we have given you the answers to your queries like Devops engineer salary for freshers, DevOps engineer salary in India for 2 years experience, DevOps engineer salary in India per month, DevOps engineer salary in India for freshers, DevOps engineer salary per month, DevOps engineer salary in India for 3 years experience etc. If you’re interested to learn more about DevOps, full-stack software development, check out upGrad & IIIT-B’s PG Diploma in Full-stack Software Development which is designed for working professionals and offers 500+ hours of rigorous training, 9+ projects, and assignments, IIIT-B Alumni status, practical hands-on capstone projects & job assistance with top firms.

by Mukesh Kumar

19 May 2024

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In an era dominated by Big Data, one cannot imagine that the skill set and expertise of traditional Data Analysts are enough to handle the complexities of Big Data. So, who then jumps in to save the day? Essentially, Big Data Analysts are data analysts in the truest sense of the term, but they have a significant point of difference – unlike traditional Data Analysts who mostly deal with structured data, Big Data Analysts deal with Big Data that primarily consists of raw unstructured and semi-structured data. Naturally, the job responsibilities and skill set of Big Data Analysts differs from traditional Data Analysts. To help you understand the job profile of a Big Data Analyst, we’ve created this guide containing a detailed description of job responsibilities, skill set, salary, and the career path to becoming a Big Data Analyst. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses Who is a Big Data Analyst? The job description of a Data Scientist and Big Data Analyst often overlap, since they both deal in Big Data. Just as Data Scientists are engaged in gathering, cleaning, processing, and analyzing Big Data to uncover meaningful insights and patterns, Big Data Analysts are responsible for understanding those insights and identifying ways to transform them into actionable business strategies. Big Data Analysts have one fundamental aim – to help businesses realize the true potential of Big Data in positively influencing business decisions. Big Data Analysts gather data from multiple sources and company data warehouses and analyze and interpret the data to extract such information that can be beneficial for businesses. They must visualize and report their findings by preparing comprehensive reports, graphs, charts, etc. Visual representation of the data findings helps all the stakeholders (both technical and non-technical) to understand it better. Once everyone can visualize the idea clearly, the entire IT and business team can brainstorm as to how to use the insights in improving business decisions, boosting revenues, influencing customer decisions, enhancing customer satisfaction, and much more. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Big Data Analysts are also summoned by businesses to perform competitive market analysis tasks to identify key industry trends. To analyze and interpret data, Big Data Analysts spend a lot of time working with a host of business, Big Data, and analytics tools like Microsoft Excel, MS Office, SAS, Tableau, QlikView, Hadoop, Spark, MongoDB, Cassandra, Hive, Pig, R, Python, SQL, to name a few. What are the job responsibilities of a Big Data Analyst? Now that you have a fair understanding of the job profile of a Big Data Analyst, let’s look at their core duties: To gather and accumulate data from disparate sources, clean it, organize it, process it, and analyze it to extract valuable insights and information. To identify new sources of data and develop methods to improve data mining, analysis, and reporting. To write SQL queries to extract data from the data warehouse. To create data definitions for new database files or alterations made to the already existing ones for analysis purposes. To present the findings in reports (in table, chart, or graph format) to help the management team in the decision-making process. To develop relational databases for sourcing and collecting data. To monitor the performance of data mining systems and fix issues, if any. To apply statistical analysis methods for consumer data research and analysis purposes. To keep track of the trends and correlational patterns among complex data sets. To perform routine analysis tasks to support day-to-day business functioning and decision making. To collaborate with Data Scientists to develop innovative analytical tools. To work in close collaboration with both the IT team and the business management team to accomplish company goals. What are the skills required to become a Big Data Analyst? Programming A Big Data Analyst must be a master coder and should be proficient in at least two programming languages (the more, the merrier). Coding is the base for performing numerical and statistical analysis on large data sets. Some of the most commonly used programming languages in Big Data analysis are R, Python, Ruby,  C++, Java, Scala, and Julia. Start small and master one language first. Once you get the hang of it, you can easily pick up other programming languages as well. Quantitative Aptitude  To perform data analysis, you must possess a firm grasp over Statistics and Mathematics including Linear Algebra, Multivariable Calculus, Probability Distribution, Hypothesis Testing, Bayesian Analysis, Time Series and Longitudinal Analysis, among other things. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Checkout: Data Analyst Salary in India Knowledge of computational frameworks The job of a Big Data Analyst is a versatile one. Thus, you need to be comfortable in working with multiple technologies and computational frameworks including both basic tools like Excel and SQL as well as advanced frameworks like Hadoop, MapReduce, Spark, Storm, SPSS, Cognos, SAS, and MATLAB. Data warehousing skills Every Big Data Analyst must have proficiency in working with both relational and non-relational database systems such as MySQL, Oracle, DB2, NoSQL, HDFS, MongoDB, CouchDB, Cassandra, to name a few. Business acumen What good would be the findings of Big Data Analysts if they couldn’t visualize them from a business angle? To use the extracted insights to transform businesses for the better, a Big Data Analyst must possess an acute understanding of the business world. Only then can they identify potential business opportunities and use the data findings to steer the business in a better direction. Communication Skills As we mentioned earlier, Big Data Analysts must know how to effectively convey and present their findings for the ease of understanding of others. Hence, they need to possess impeccable written and verbal communication skills so that they can explain their vision to others and breakdown complex ideas into simpler terms. Data Visualization  Trends, patterns, and outliers in your data are communicated through visualizations. If you’re relatively fresh to data analysis and searching for an initiative, making visualizations is a wonderful place to start. Choose graphs that are appropriate for the narrative you’re attempting to portray. Bar and line charts portray changes over duration concisely, whereas pie charts represent part-to-whole correlations. On the other hand, histograms and bar charts depict data distribution.  An instance of data visualization is when data analyst Han visualizes the level of expertise needed in 60 distinct activities to determine which one is the most difficult. Following are some excellent examples of data analytics projects for beginners using data visualization: Astronomical Visualization History Visualization Instagram Visualization Here are a few free data visualizations tools you can use for your data analyst projects: Google Charts This data visualization tool and interactive chart gallery make it simple to insert visualizations inside a portfolio using JavaScript code and HTML. A comprehensive Guides feature walks you through the design process. RAW Graphs This free and open-source web application makes it simple to convert CSV files or spreadsheets into a variety of chart kinds that would be challenging to create otherwise. You can even play with example data sets provided by the program. Data-Driven Documents or D3 You can accomplish a lot using this JavaScript package if you have basic technical knowledge. Data Mining The procedure of converting raw data into meaningful information is known as data mining. One of the data mining assignments you can conduct to boost your data analyst portfolio is Speech Recognition. Speech recognition software recognizes spoken words and converts them to written content. Install speech recognition programs in Python, such as SpeechRecognition, Watson-developer-cloud, or Apiai. DeepSpeech is a free and open-source engine that is based on TensorFlow by Google. You can use the application to convert speeches into texts. Another data analytics example using data mining is Anime Recommendation System. While streaming recommendations are great, why not create one targeting a certain genre? You can make use of user preference data and develop multiple recommendation systems by categorizing related shows based on characters, reviews, and synopses.  Natural Language Processing (NLP) NLP is an area of artificial intelligence that assists computers in interpreting and manipulating natural language in the manner of audio and text. To acquire a higher senior-level role, try to include some of the following NLP projects works in your portfolio: Autocorrect and Autocomplete In Python, you can generate a neural network that autocompletes phrases and identifies grammatical problems.  News Translation Python can be used to create a web-based program that converts news from one particular language into another. Salary of a Big Data Analyst According to Glassdoor, the average salary of a Big Data Analyst is Rs. 6,54,438 in India. The salary of Big Data professionals depends on many factors including educational background, level of expertise in Big Data, years of working experience, and so on. The entry-level salaries can be anywhere between 5 – 6 LPA, the salary increases exponentially with experience and upskilling. Experienced Big Data Analyst can earn as high as 25 LPA, depending upon the company they work for. Steps to launch a career as a Big Data Analyst Here’s how you can launch your career as a Big Data Analyst in three simple steps: Graduate with a Bachelor’s degree with STEM (science, technology, engineering, or math) background. While the job profile of a Big Data Analyst doesn’t demand highly advanced degrees,  most companies look for candidates who’ve graduated with a Bachelor’s degree with a specialization in STEM subjects. This is the minimum selection criteria for the job, so you have to make sure you attain it. Learning STEM subjects will introduce you to the fundamentals of Data Science, including programming, statistical, and mathematical skills. As for project management and database management, you can take special classes for these. Get an internship or entry-level job in data analysis. While it is difficult to bag data analysis jobs with zero experience in the field, you must always be on the lookout for opportunities. Many institutions or companies offer internship programs in data analysis, which could be a great start to your career. Then there are also various in-house training programs in Big Data Management, Statistical Analysis, etc. Enrolling into such programs will help you gain the necessary skills required for data analysis. Another option is to look for entry-level jobs related to this field, such as that of a Statistician, or a Junior Business Analyst/Data Analyst. Needless to say, these positions will not only help further your training, but they will also act as a stepping stone to a Big Data career. Get an advanced degree. Once you obtain working experience, it is time to amp up your game. How so? By getting an advanced degree like a Master’s degree in Data Science, or Data Analytics, or Big Data Management. Having an advanced degree will enhance your resume and open up new vistas for employment in high-level data analysis positions. Naturally, your prospective salary package will also increase by a great extent if you have a Master’s degree or equivalent certification. Preparing for a data analyst role? Sharpen your interview skills with our comprehensive list of data analyst interview questions and answers to confidently tackle any challenge thrown your way. Job Outlook of Big Data Analysts According to the predictions of the World Economic Forum, Data Analysts will be in high demand in companies all around the world. Furthermore, the US Bureau of Labor Statistics (BLS) maintains that employment opportunities for market research analysts, including Data Analysts, will grow by 19% between 2014 to 2024. This is hardly surprising since the pace at which data is increasing every day, companies will need to hire more and more skilled Data Science professionals to meet their business needs. All said and done, the career outlook of Big Data Analysts looks extremely promising. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? What are you waiting for? We’ve provided you with all the vital information you need to gear up for a career as a Big Data Analyst. The ball’s in your court! If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.

by Mukesh Kumar

16 Dec 2023

Blogs

1784

Systems Engineering is an interdisciplinary approach and systematic methodology focusing on the comprehensive design, development, and lifecycle management of intricate systems. Complex or large-scale projects bring challenges across diverse domains such as requirements engineering, logistical support, team coordination, and risk management. Negotiating these complexities requires adeptness in engineering, business, and managerial realms. Specialised training, such as IT Service Management, can enhance skills and open doors to career advancement. By upskilling, you can optimise your potential and maximise your earning potential.  This article explores the spectrum of system engineer salary figures while offering insights into the strategies to help you excel as a proficient and accomplished system engineer. An opportunity like upGrad’s  Full Stack Software Development Bootcamp can provide the knowledge and expertise needed, enabling you to navigate the intricate landscape of systems engineering. Who is a System Engineer? Systems engineers play a pivotal role in the comprehensive development and oversight of intricate systems, offering solutions to complex problems from system inception through production and ongoing management.  As a systems engineer, you can assume responsibility for troubleshooting and effective communication among all system implementation and utilisation stakeholders. Systems engineering extends across diverse sectors, from healthcare and transportation to manufacturing and software industries. This profession thrives on unravelling challenges, exploring innovative avenues, and constructing systems that effectively address issues or accomplish objectives. Within the United States, the projected annual remuneration for a System Engineer is approximated at USD 116,141. While in India, the salary spectrum for System Engineers spans from INR 2.5 Lakhs to INR 7.8 Lakhs, culminating in an average annual remuneration of INR 4.4 Lakhs.  System Engineer Job Roles Systems Engineers shoulder a comprehensive array of responsibilities, encompassing: Assessing system landscapes, diagnosing issues, and delivering effective solutions. Crafting system designs and orchestrating seamless upgrades. Sustaining and nurturing system vitality with an eye toward future enhancements. Collaborating adeptly with diverse stakeholders, including clients, vendors, management, and IT professionals, throughout system inception and upkeep. Constructing robust control mechanisms to uphold organisational quality benchmarks. Enforcing rigorous quality control protocols to ensure optimal system efficiency and functionality. Formulating economically viable system strategies through methodical cost-benefit analyses. This multifaceted job of a Systems Engineer underscores their role in propelling systems’ performance, fortifying quality, and fostering innovation within dynamic operational frameworks. Factors Affecting System Engineer Salary in India Here are some factors that affect the system engineer salary in India 1. Based On Experience In formulating a remuneration framework, experience assumes paramount significance. The financial compensation accrued is intricately linked to one’s depth of expertise, a culmination derived from practical experience.  Earnings are inherently proportional to the wealth of knowledge, affirming that a higher echelon of wisdom invariably means higher financial gains. Annual salary benchmarks for Systems Engineers in the IT sector are as follows: Novice Systems Engineer with less than 1 year of expertise anticipates an average annual remuneration of INR 3,32,518. Early-career Systems Engineer boasting 1-4 years of industry exposure receives an average total income of INR 3,89,284 annually. Mid-career Systems Engineer enriched by 5-9 years of professional trajectory receives an average annual package of INR 5,57,489. Accomplished Systems Engineer fortified with 10-19 years of seasoned practice, enjoys an average yearly compensation of INR 7,99,780. 2. Based on Location In India, the annual average salary for a system engineer amounts to INR 5,09,000. The salary ranges for system engineers across different cities are detailed as follows: City Average Systems Engineer Salary Noida INR 8,38,812 Hyderabad INR 7,42,138 Chennai INR 6,37,324 Bengaluru INR 5,62,643 Mumbai INR 4,93,813 Mysore INR 4,17,719 Gurugram INR 3,47,314 Pune INR 3,82,708 Check out our free technology courses to get an edge over the competition. 3. Based On the Company Below, we present a compilation of prominent companies within the domain of systems engineering, along with the corresponding salaries they extend for this role. Qualcomm, a technology giant, offers its employees an attractive embedded software engineer salary of INR 12,34,000. Infosys, a renowned IT services company, provides a good package to their employees. The starting Infosys system engineer salary is INR 3,90,000, which goes beyond this package. Moreover, the senior system engineer’s salary in Infosys is INR INR 4,81,261. Tech Mahindra, a leading IT solutions provider, offers its employees an annual compensation package of INR 4,07,359. Wipro, another prominent IT company, presents its workforce with an annual salary package totaling INR 4,04,000. AMCAT, known for its recruitment services, provides its talented employees with an annual salary package of INR 4,47,087. IBM, a prominent multinational technology company, offers its employees an attractive annual salary package. The average IBM associate system engineer salary in India is INR 7,47,083. Siemens, a multinational conglomerate, is generous. The annual embedded systems engineer salary in Siemens is INR 7,10,000 for its dedicated employees. Cerner, a healthcare information technology company, rewards its workforce with an annual compensation package of INR 6,60,000. TCS, a major player in the IT industry, offers its employees a decent package. The average system engineer salary in TCS is INR 4,54,842. Moreover, the average assistant system engineer TCS salary ranges from INR 2,40,000 to INR 5,80,000 per annum. Intel, a leading semiconductor company, provides its skilled professionals with an impressive annual salary package of INR 12,55,880. Check Out upGrad’s Software Development Courses to upskill yourself. How to Improve System Engineer Salary? When figuring out how you can claim lucrative pay and a boost in your income, listed below are some tips that can help you obtain handsome pay and better opportunities. Here are the things you can do to improve your system engineer salary spectrum: Skills  Engineers excel in meticulous problem-solving but have often been labelled as needing more communication and social skills. Engineers adept at interpersonal interactions, representing their firms effectively, and assuming marketing responsibilities are often rewarded with better pay.  Strong people skills can propel engineers into top decision-making roles, amplifying their earning potential. Proficiency in business development and social understanding significantly contributes to salary growth.    Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses Certifications Elevating the salary prospects of senior systems engineers often involves pursuing advanced certifications. For instance, a foundational certification in coding is a cornerstone for system development, equipping engineers with essential coding and technical proficiencies. These certifications, whether pursued online or offline, facilitate entry and growth within the field.  upGrad’s comprehensive certification, the Full Stack Developer Course – Executive PG Program in Software Development, aligns with these principles, empowering professionals to excel in this dynamic sphere while substantiating their expertise. Degrees Many enterprises prefer candidates with a master’s degree in systems engineering or a closely associated discipline. Such an advanced degree notably enhances suitability, especially in managerial capacities. After your bachelor’s degree, you can get a master’s degree in computer science, focusing on specific areas that interest you.   Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Experience Having the right job experience that matches your skills can help you earn more money and improve the embedded system engineer salary each year. This means focusing on the things you’re good at. It’s important to explain how you used those skills in your previous jobs, which can help the company in the future.  Pursuing an internship is a good idea, too, because companies like hiring people who have already worked in the field. Even if you complete an internship while you’re in school or after you finish, it gives you an edge by teaching you how things work in real businesses. To Sum Up The role of a systems engineer is undeniably multifaceted, encompassing the intricate design, development, and management of complex systems. While experience remains a key determinant of system engineer salary, honing skills and pursuing advanced certifications can notably elevate earning potential.  To embark on a transformative journey in systems engineering, consider upGrad’s Master of Science in Full Stack Software Development Bootcamp. This comprehensive program equips you with the essential knowledge and skills to excel in this dynamic field, positioning you for rewarding opportunities and career advancement.  Elevate your expertise and seize the future of systems engineering with upGrad! FAQs  

by Mukesh Kumar

24 Sep 2023

Blogs

5881

The fast-moving world laced with technology has created a convenient environment for companies to provide better services to their clients. Cloud computing is one aspect brands strive hard to incorporate into their services. The service accounts for easier accessibility and storage options for customers to sustain their requirements at any time of the day without needing to be in touch with their hefty complex storage devices.  The prevalence of cloud computing services in our daily lives has created a demand for skilled experts. Consequently, the cloud computing industry is hovering for a prosperous future. Experienced professionals seeking to establish themselves in this domain are in high demand. The rapid assimilation of cloud computing services into our daily lives has led to the need for learned professionals owning expertise in the domain. As a result, the cloud computing industry has a bright future, and the current market demand is exceptional for experienced people looking out to make a name in the zone.  If you wish to be a part of this globally emerging market, you must have one significant skill necessary to perform the role well. AWS, or Amazon Web Services, is a cloud computing platform companies rely on for their storage, server, networking, security, remote computing, and many such features used by leading industry brands such as Netflix and Adobe. Thus, to make a career in cloud computing, you must excel in AWS interviews. Here are some of the AWS interview questions and answers to help you prepare for the big day! 10 Questions and Answers for AWS Interview  Q1: Explain what is AWS? Amazon provides a complete cloud computing platform known as Amazon Web Services (AWS).It offers many scalable and cost-effective services, including computing power, storage options, and databases where you can access these services through the internet. The advantage of AWS is that users can quickly deploy and manage applications without investing in hardware upfront. By taking care of infrastructure management, security, and maintenance complexities, AWS allows businesses and developers to focus on innovation and growth. AWS offers dependable and adaptable cloud solutions to businesses of any scale and industry, with data centers located worldwide. Q2: What are the primary services offered by AWS? Services offered by AWS can be divided into four categories. These categories constitute networking, computing, storage, and security. Each of these four includes several cloud-run products curated for cross-functional uses by individuals, companies, and governments. Some of them include EC2, Lamba, CloudFront, Elastic File System, VPC, LightSat, etc., joining together to deliver attributes of a real computer but on the cloud. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career. Q3: What is S3? Amazon S3, also known as Amazon Simple Storage Service, is a storage solution based in the cloud. It allows users to conveniently store and retrieve data anywhere and anytime through a web service interface. With its scalability and durability, Storing large amounts of data is made easy with Amazon S3. It is an excellent option to consider.This platform is widely used by developers worldwide. Users can enjoy the benefits of this reliable online backup service with a flexible pricing model that allows them to pay only for the storage they use. Q4: What is Cloudfront? Cloudfront is a content delivery network comprising the distribution of proxy servers globally, as people can access content data based on their geographical location. Cloudfront follows geotargeting as it lets the servers curate content on their webpage based on people’s geographical data without changing the URL. Also, the servers cache content and allow local users easy access to stream or download web content.  Q5: What is AWS SNS? The Simple Notification Service (SNS) provided by Amazon Web Services allows publishers to efficiently send notifications to subscribers using a push notification system. It is designed to support various communication scenarios between applications and individuals, making it a versatile solution for multiple purposes.  SNS ensures high availability by replicating messages across multiple availability zones, which improves reliability. Its user-friendly interface and APIs make integration with existing applications simple. With its global infrastructure, SNS offers fast message delivery with low latency worldwide. This scalable and dependable service plays a crucial role in real-time information sharing, making it an indispensable tool for modern cloud-based architectures and seamless communication between applications and users. Q6: What is AWS Disaster Recovery Solution? AWS Disaster Recovery Solution is the most prominently known AWS feature that helps reduce data loss due to its fast, reliable recovery systems dealing with possible network failures or attacks. In addition, disaster Recovery Solution creates backups or data replicas to keep data saved under any uncertain times. The program also helps generate better productivity as it is cost-effective, high-performance, and replicates data to protect from seasonal fluctuations in lower recovery times. Q7: What is Cloudformation in AWS? Juggling various AWS services can get complicated. Hence, Cloudformation is created to solve concerns with its library full of templates and extra AWS resources. Instead of seeking resources with every possible use, a library full of templates is easier to access. The program collects AWS resources to manage them in an organized manner for businesses and developers.  Q8: What are Availability zones and Regions in AWS? Availability zone in AWS stands for the geographical area where cloud services are centered and operate with redundant power and networking infrastructure. These availability zones are present inside the regions. The difference between these two is that the region constitutes a separate geographical area while the availability zone comprises multiple isolated places within regions.  Q9: Explain VPC in AWS. Virtual Private Cloud of VPC in AWS is a cloud computing service that provisions isolated networking clouds for better connectivity and networking. What sets it apart from the AWS service is that you have full control over your virtual experience. This means you can customize and use the network configuration according to your requirements. Furthermore, the virtual private cloud ensures protection against any interference or public access. Q10: What is the VPC-Peering Connection? VPC peering establishes a connection between two VPCs enabling it to route traffic between them. For example, creating a VPC peering connection can help transfer heavy files over the created link. However, VPC peering connection can only be established if the receiver accepts or rejects the request. How to prepare for the AWS interview? Besides practicing interview questions and answers, follow the basic interview preparation structure to stay ready. The essentials for the interview are researching about the company, comprehending the job description, and creating a well-formatted application. Finally, review fundamental AWS lessons for a quick revision. Make sure you know the basics well and answer the questions with utmost confidence and precision. Ensure you know the basics well and answer the questions confidently and precisely. If you wish to gain more insight and experience in the relevant sphere, head over to upGrad’s online program. upGrad offers a Master of Science in Computer Science program by Liverpool’s John Moores University, created for learners worldwide to earn proficiency in computer science specialization fields including Cyber Security, Full Stack Development, and Cloud Computing. The program is specifically designed to educate learners on up-to-date software development and other relevant programs to keep up with the changing times and trends.  upGrad strives to provide education that aligns with the latest market practices attracting over 40,000 learners from more than 85 countries. The curriculum, developed by industry experts and experienced faculty, integrates cutting-edge skills. Conclusion The AWS interview is no different from any other interview. As long as you have the right knowledge about its components and their apt functioning, the interview process will turn smoother. Run through all the found resources to polish your knowledge. Apart from individual preparation, digital training and certificates are some recent ways people are trying to brush up their AWS skills. 

by Mukesh Kumar

07 Sep 2023

Blogs

6494

Thanks to improved healthcare services, today, the average human lifespan has increased to a great extent. While this is a commendable milestone for humankind, it also poses lots of new and diverse challenges for health care providers (HCPs). They face increasing amounts of challenges in delivering healthcare services to patients. This is where Big Data comes in the scenario.  Big Data in healthcare pertains to the massive amounts of healthcare data gathered from multiple sources such as pharmaceutical research, electronic health records (EHRs), healthcare wearables, medical imaging, genomic sequencing, and other such processes. The digitization of healthcare information and the increase in demand for value-based care are the primary reasons behind the rapid rise in Big Data in healthcare. As the ever-increasing pile of healthcare data continues to pose new challenges for HCPs, it calls for the adoption of Big Data technologies and tools that can efficiently collect, store, and analyze large datasets to deliver actionable insights. Rise of Big Data in Healthcare The adoption of big data use cases in healthcare has been quite slow compared to other industries (manufacturing, BFSI, logistics, etc.) due to reasons like the sensitivity of private healthcare data, security issues, and budget constraints, among other things. However, a report by the International Data Corporation (IDC) sponsored by Seagate Technology maintains that Big Data is likely to grow faster in healthcare than in sectors like media, manufacturing, or financial services. Furthermore, estimates suggest that healthcare data will grow at a CAGR of 36% all through till 2025. Currently, 2 primary trends have encouraged the adoption of big data use cases in healthcare. The first push came from the transition from the ‘pay-for-service’ model (it offers financial incentives to HCPs and caregivers for delivering healthcare services) to a ‘value-based care’ model (it rewards HCPs and caregivers according to the overall health of their patient population). This transition has been possible because of the ability of Big Data Analytics to measure and track the health of the patients.  The second trend is where HCPs and medical professionals leverage using  Big Data Analytics to deliver evidence-based information that promises to boost the efficiencies of healthcare delivery while simultaneously increasing our understanding of the best healthcare practices. Bottomline – adopting big data use cases in healthcare can potentially transform the healthcare industry for the better. It is not only allowing HCPs to deliver superior treatments, diagnosis, and care experiences, but it is also lowering healthcare costs, thereby making healthcare services accessible to the mass.   Applications of Big Data in Healthcare Health Tracking Along with the Internet of Things (IoT), Big Data Analytics is revolutionizing how healthcare statistics and vitals are tracked. While wearables and fitness devices can already detect heart rate, sleep patterns, distance walked, etc., innovations in this front can now monitor one’s blood pressure, glucose levels, pulse, and much more. These technologies are allowing people to take charge of their health.  Episode Analytics HCPs are always struggling with offering quality healthcare services at marginalized costs. Episode Analytics and Big Data tools are helping solve this dilemma by allowing HCPs to understand their performance, to identify the areas that offer scope for improvement, and to redesign their care delivery system. Together, all of this helps to optimize the processes as well as reduce the costs. Fraud detection and prevention  Big Data Analytics and tools come in very handy to detect and prevent fraud and human errors. These can validate the patient data, analyze his/her medical history, and point out any out of place errors in prescriptions, wrong medicines, wrong dosage, and other minor human mistakes, thereby saving lives. Real-time alerts Big Data tech allows HCPs and medical professionals to analyze data in real-time and perform accurate diagnoses. For instance, Clinical Decision Support (CDS) software can analyze medical data on-spot, thereby offering crucial medical advice to healthcare practitioners as they diagnose patients and write prescriptions. This helps save a lot of time. Telemedicine  Thanks to Big Data technologies, we are now able to make full use of Telemedicine. It allows HCPs and medical practitioners to deliver remote diagnosis and clinical services to patients, saving them both time and money.  Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses Big Data Analytics for Disease Diagnosis and Prediction Big data analytics has emerged as a powerful tool in healthcare for disease diagnosis and prediction. With the exponential growth in healthcare data, including electronic health records, medical imaging, genomic information, and patient-generated data, the potential to extract valuable insights has increased significantly. Here are some key aspects of how big data analytics is transforming disease diagnosis and prediction in healthcare: 1. Early Detection and Diagnosis Big data use cases in healthcare enable healthcare providers to analyze large datasets from diverse sources, helping identify patterns, trends, and anomalies that may indicate early signs of diseases. By analyzing patient data, including vital signs, lab results, and lifestyle information, healthcare professionals can identify high-risk individuals and intervene proactively, leading to early diagnosis and timely treatment. 2. Predictive Analytics for Patient Outcomes Through predictive analytics, big data helps healthcare institutions anticipate patient outcomes, treatment responses, and disease progression. By employing machine learning algorithms on vast amounts of patient data, healthcare providers can create predictive models that estimate the likelihood of specific outcomes based on individual patient characteristics, historical data, and treatment options. 3. Precision Medicine Big data analytics is critical in advancing precision medicine, tailoring medical treatments to individual patients based on their genetic makeup, lifestyle, and other relevant factors. Analyzing massive genomic datasets allows researchers and clinicians to identify genetic markers associated with specific diseases and determine personalized treatment strategies that offer individual patients the highest chances of success. 4. Real-time Data Monitoring Big data analytics enables real-time patient data monitoring, offering healthcare professionals a continuous and comprehensive view of a patient’s health status. This real-time data access facilitates prompt detection of any alarming changes, allowing for timely interventions and reducing the risk of complications. 5. Disease Outbreak Prediction and Management In public health, big data use cases in healthcare assists in disease outbreak prediction and management. By analyzing data from various sources, including social media, surveillance systems, and patient records, public health authorities can identify and respond to potential outbreaks more swiftly and effectively, helping to control the spread of infectious diseases. 6. Drug Discovery and Development Big data analytics is accelerating drug discovery and development processes in the pharmaceutical industry. Researchers can identify potential drug targets, predict drug efficacy, and optimize treatment regimens by analyzing vast datasets, including molecular information, clinical trial results, and drug interactions. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Enhancing Personalized Medicine through Big Data Insights Big data use cases in healthcare insights have significantly advanced personalized medicine, tailoring medical treatments to individual patients based on their unique characteristics. Here’s how big data insights are enhancing personalized medicine: 1. Patient Profiling and Risk Stratification Big data analytics allows healthcare providers to create detailed patient profiles by analyzing vast patient data, including medical history, genetic information, lifestyle factors, and treatment outcomes. These profiles enable risk stratification, identifying patients at higher risk for specific diseases or adverse treatment reactions. Healthcare professionals can develop personalized prevention plans and treatment approaches by understanding individual patient risk factors. 2. Genomics and Precision Medicine Big data analysis of genomic data is crucial in advancing precision medicine. Researchers can identify genetic variations associated with certain diseases or drug responses by analyzing large-scale genomic datasets. This information helps develop targeted therapies that are more likely effective and reduce the risk of adverse reactions. 3. Treatment Response Prediction Big data analytics leverages machine learning algorithms to analyze patient data and predict individual treatment responses. By considering genetic markers, clinical history, and lifestyle factors, healthcare providers can determine the most suitable treatment options for each patient, increasing the chances of successful outcomes. 4. Real-time Monitoring and Wearable Devices With the proliferation of wearable devices and IoT-enabled healthcare solutions, big data use cases in healthcare insights enable real-time monitoring of patient’s health parameters. Continuous data collection and analysis provide healthcare professionals with up-to-date information about patients’ conditions, facilitating timely adjustments to treatment plans based on their evolving health status. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Wrapping up In the future, the healthcare sector will see a lot more of Big Data applications that will revolutionize the healthcare industry one step at a time. Not only will Big Data help streamline the delivery of healthcare services, but it will also allow HCPs to enhance their competitive advantage through smart business solutions. If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.

by Mukesh Kumar

28 Aug 2023