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6584

What is Apache Spark?  Apache Spark is a bunch of computing framework intended for real-time open-source data processing. Fast computation is the need of the hour and Apache spark is one of the most efficient and swift frameworks planned and projected to achieve it.  The principal feature of Apache Spark is to increase the processing speed of an application with the assistance of its in-built cluster computing. Apart from this, it also offers interface for programming complete clusters with various aspects like implicit data parallelism and fault tolerance. This provides great independence as you do not need any special directives, operators, or functions, which are otherwise required for parallel execution. Important Expressions to Learn Spark Application – This operates codes entered by users to get to a result. It works on its own calculations. Apache SparkContext – This is the core part of the architecture. It is used to create services and carry out jobs. Task – Every step has its own peculiar task that runs step by step. Apache Spark Shell – In simple words, it is basically an application. Apache Spark Shell is one of the vital triggers on how data sets of all sizes are processed with quite ease. Stage – Various jobs, when split, are called stages.  Job – It is a set of calculations that are run parallelly. Gist of Apache Spark Apache Stark is principally based on two concepts viz. Resilient Distributed Datasets (RDD) and Directed Acyclic Graph (DAG). Casting light on RDD, this comes to light that it is a stock of data items broken and saved on worker nodes. Hadoop datasets and parallelized collections are the two RDDs that are supported.  The earlier one is for HDFS whereas the latter is for Scala gatherings. Jumping to DAG – it is a cycle of mathematical calculations conducted on data. This eases the process by getting rid of the multiple carrying out of operations. This is the sole reason Apache Spark is preferred over Hadoop. Learn more about Apache Spark vs Hadoop Mapreduce. 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 Spark Architecture Overview Before delving deeper, let us go through the architecture. Apache Spark has a great architecture where the layers and components are loosely incorporated with plenty of libraries and extensions that do the job with sheer ease. Chiefly, it is based on two main concepts viz. RDD and DAG. For anyone to understand the architecture, you need to have a sound knowledge of various components such as Spark Ecosystem and its basic structure RDD. Features of Apache Spark architecture The goal of the development of Apache Spark, a well-known cluster computing platform, was to speed up data processing applications. Popular open-source framework Spark uses in-memory cluster computing to speed up application performance.  Here are some features of Spark architecture- Strong Caching: A simple programming layer provides effective disk durability and cache features. Real-Time: It enables minimal latency and real-time computation due to its in-memory processing. Deployment: It may be deployed via Mesos, Hadoop through YARN, or Spark’s own cluster management. Polyglot: Spark also supports Python, R, Scala, and Java in addition to these four other languages. Any one of these languages may be used to create Spark code. Additionally, Python and Scala command-line interfaces are offered by Spark. Speed: For processing massive volumes of data, Spark is up to 100 times quicker than MapReduce. Additionally, it has the ability to break the data into manageable bits. Apache Spark Has Two Main Abstractions The layered architecture of Apache Spark is clearly defined and built around two fundamental abstractions: Resilient Distributed Datasets (RDD) It is an essential tool for computing data. It serves as an interface for immutable data and allows you to double-check the data in the case of a failure. It is a kind of data structure that aids in data recalculation in case of errors. RDDs can be altered using either transformations or actions. Directed Acyclic Graph (DAG)  Stage-oriented scheduling is implemented by the DAG scheduling layer of the Apache Spark architecture. For each job, the driver transforms the program into a DAG. A driver is a series of connections made between nodes. Modes of Execution The physical locations of the resources indicated before can be ascertained using an execution model. There are three execution modes available for selection: Cluster Mode The most popular approach to execute Spark Applications is in cluster mode. The driver process is launched on a worker node inside the cluster together with the executor processes as soon as the cluster manager gets the pre-compiled JAR, Python script, or R script. This indicates that all Spark application-related processes are under the control of the cluster manager. Client Mode The only difference between client mode and cluster mode is that the Spark driver stays on the client computer that made the application submission. So, the executor processes are maintained by the cluster management, and the Spark driver processes are maintained by the client computer. Common names for these devices include edge nodes or gateway nodes. Local Mode The complete Spark program runs on a single computer in local mode. The use of threads on that same system allows for the observation of parallelism. This simple procedure makes it simple to experiment with local development and test apps. However, it is not advised to run production applications in this manner. Advantages of Spark This is one of the platforms that is entirely united into a whole for a couple of purposes – to provide backup storage of unedited data and an integrated handling of data. Moving further, Spark Code is quite easy to use. Also, it is way easier to write. It is also popularly used for filtering all the complexities of storage, parallel programming, and much more.  Unquestionably, it comes without any distributed storage and cluster management, though it is quite famous for being a distributed processing engine. As we know, both Compute engine and Core APIs are its two parts, yet it has a lot more to offer – GraphX, streaming, MLlib, and Spark SQL. The value of these aspects is not unknown to anyone. Processing algorithms, ceaseless processing of data, etc. bank on Spark Core APIs solely. Working of Apache Spark A good deal of organizations needs to work with massive data. The core component that works with various workers is known as driver. It works with plenty of workers that are acknowledged as executors. Any Spark Application is a blend of drivers and executors. Read more about the top spark applications and uses. Spark can cater to three kinds of work loads Batch Mode – Job is written and run through manual intervention. Interactive Mode – Commands are run one by one after checking the results. Streaming Mode– Program runs continuously. Results are produced after transformations and actions are done on the data. Spark Ecosystem and RDD To get the gist of the concept truly, it must be kept in mind that Spark Ecosystem has various components – Spark SQL, Spark streaming, MLib (Machine Learning Library), Spark R, and many others. When learning about Spark SQL, you need to ensure that to make the most of it, you need to modify it to achieve maximum efficiency in storage capacity, time, or cost by executing various queries on Spark Data that are already a part of outer sources. After this, Spark Streaming allows developers to carry out both batch-processing and data streaming simultaneously. Everything can be managed easily.  Furthermore, graphic components prompt the data to work with ample sources for great flexibility and resilience in easy construction and transformation.  Next, it comes to Spark R that is responsible for using Apache Spark. This also benefits with distributed data frame implementation, which supports a couple of operations on large data sets. Even for distributed machine learning, it bids support using machine learning libraries. Finally, the Spark Core component, one of the most pivotal components of Spark ecosystem, provides support for programming and supervising. On the top of this core execution engine, the complete Spark ecosystem is based on several APIs in different languages viz. Scala, Python, etc.  What’s more, Spark backs up Scala. Needless to mention, Scala is a programming language that acts as a base of Spark. On the contrary, Spark supports Scala and Python as an interface. Not just this, the good news is it also bids support to interface. Programs written in this language can also be performed over Spark. Here, it is to learn that codes written in Scala and Python are greatly similar. Read more about the role of Apache spark in Big Data. Spark also supports the two very common programming languages – R and Java.  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 Conclusion Now that you have learned how the Spark ecosystem works, it is time you explored more about Apache Spark by online learning programs. Get in touch with us to know more about our eLearning programs on Apache Spark. If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Check our other Software Engineering Courses at upGrad.

by Utkarsh Singh

20 Jun 2023

Blogs

7515

Every day, the world creates over 2.5 quintillion bytes of data, and estimates suggest that by the end of 2020, each second every person will create nearly 1.7MB of data! With Big Data growing at an unprecedented pace, it is becoming crucial to organize, clean, analyze, and visualize this data to gain the ultimate business advantage. If businesses don’t analyze, interpret, and visualize data, it holds no value in the real-world. This is why data visualization is gaining increasing traction in the business and tech worlds. Data visualization tools and techniques allow enterprises and organizations to represent the extracted insights and findings in ways that can be understood by all stakeholders involved in the business, particularly the non-technical members. Essentially, data visualization radically changes the way businesses and organizations access, present, and use data.  Top Benefits of Data Visualization When implemented right, data visualization has numerous benefits to offer. In this post, we’ll shed light on the most important benefits of data visualization. 1. It promotes improved absorption of business information Perhaps one of the most pivotal advantages of data visualization is that it facilitates the easy and quick assimilation of colossal amounts of Big Data. Since the human eye can process visual images nearly 60,000 times faster than text or numbers, data visualization is the best way to absorb and process information. The human brain finds it easier to process visual representation of data like graphs or charts and further convert this information into a mental visualization.  Data visualization enables business owners and decision-makers to identify meaningful and valuable connections between multi-dimensional data sets and provides new ways of interpreting data through the various forms of graphical representations (bar graphs, histograms, pie charts, PowerPoint presentation, etc.) 2. It provides quick access to meaningful business insights. When organizations and businesses adopt visual data discovery tools and techniques, it allows them to improve their ability to extract relevant insights from within large datasets. Data visualization tools help the business identify relevant patterns and trends hidden in the data.  Extracting and uncovering these hidden trends and insights helps organizations to maintain their relevance in this increasingly competitive industry. Such patterns can show crucial business information like losses, profits, ROI, and much more. With this information at hand, businesses can fine-tune their business decisions accordingly to maximize gains and boost customer satisfaction. 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 3. It offers an improved understanding of business operations. In the present competitive business environment, it has become imperative for businesses to find meaningful correlations in the data, and data visualization is the tool to accomplish this. Data visualization enables organizations to clearly see the connections and relations between the various business operations as and when they occur. It offers a multi-faceted view of business and its operating dynamics, thereby enabling senior leadership and management teams to manage routine business operations productively.  By using data visualization tools, managers and decision-makers can quickly understand and analyze critical business metrics. It also displays anomalies in these metrics so that decision-makers can dig deep into their business data and see what operational conditions are at work and how they can be improved for maximum productivity and gains. Read More: Data Visualization in R programming 4. It helps to communicate business insights in a constructive manner. Usually, business reports comprise of formal documents containing lengthy explanations, static tables, and different types of charts. Such reports are so detailed and elaborate that they often fail to highlight critical information. However, this is not the case with data visualization tools. The reports generated via data visualization tools not only represent the valuable business findings but also encapsulate complex information on operational and market conditions through a series of graphical forms.  The interactive elements and visualization formats (heat maps, fever charts, etc.) help decision-makers to quickly interpret the information extracted from a wide variety of disparate data sources. Rich graphics of data visualization reports allow business executives to not only find new business solutions but also speed up pending strategies. 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 5. It facilitates prompt identification of the latest business trends. With data growing as we speak, businesses must be competent enough to gather data quickly and uncover insights from it in real-time. Failing to do so will mean a loss of business opportunities. The faster you can analyze data, extract insights, and realize them into actionable business decisions, the stronger will be your foothold in the industry.  Data visualization can capture pivotal business data like shifts in customer behaviour, changes in market trends across multiple datasets, changes in business performance, and much more. These trends play an important role in shaping future business decisions and strategies.  6. It can capture customer sentiment analysis accurately. Just as data visualization can depict the latest trends in the market and offer insights into customer behaviour, it can also read into customer sentiments. Data visualization tools can dig deep into customer sentiments and other customer-oriented data to reveal what they think about your brand, what they speak about your brand in social media platforms, what they do to spread awareness for your brand, and so on. Read more about the sentiment analysis in big data. By doing so, it allows businesses to get into the psyche of their target audience, understand their pain points, their likes and dislikes, and their preferences. They can use this information to shape business strategies, marketing strategies, product ideas, and brand outreach ideas accordingly. Furthermore, these insights pave the way for numerous unique business opportunities. 7. It encourages interaction with data. One of the starkest traits of data visualization is that it encourages and promotes direct interaction with data. When companies gather data from multiple sources, data visualization tools help organizations to manipulate and interact with their business data directly in ways that can produce actionable insights unique to the business. Data visualization helps bring meaningful insights to the surface through multi-dimensional representations that allow you to view data from multiple perspectives, unlike one-dimensional tables and charts. Overall, data visualization creates the perfect opportunity for companies to engage with their data to design actionable business solutions actively. 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 8. It helps save valuable employee time. Before data visualization came to the picture, employees had to spend a significant amount of their work hours in creating detailed reports, modifying dashboards, responding to ad hoc data retrieval requests, and so much more.  This is not only time consuming but also cumbersome. Companies need to provide explicit training to their employees to use the appropriate systems and software to create reports, manage dashboards, etc. These 10 data visualization types help employees save lot of time. Thanks to Big Data visualization techniques and tools, a lot of routine tasks are automated. With advanced data visualization tools, you can retrieve data instantly and with hardly any effort. This helps save valuable employee time, thereby allowing the businesses to focus their human resources on tasks that demand human cognition and intelligence. Importance of Data Visualization In today’s data-driven world, data visualization helps organizations analyze complex data, identify patterns, and extract valuable insights. One of the biggest advantages of data visualization is that it simplifies the vast amounts of detailed information and presents it visually, helping decision-makers make informed choices that benefit their organizations. Communicating data findings, identifying patterns, and interacting seamlessly with data without effective visualization becomes challenging. The scope of data visualization extends to every industry because understanding and leveraging data is vital for all businesses. Information is the most powerful asset for any organization, and through visualization, one can effectively convey key points and extract valuable information from the data. Here is an overview of the main benefits of big data visualization MCQ. Enhanced Data Analysis Data visualization allows stakeholders to identify areas that need immediate attention. Visual representation helps data analysts grasp essential components that are vital to the business. That could include forming marketing strategies or analyzing sales reports. When data is presented visually, companies can actively work to improve their profits because they can analyze their shortcomings better and make informed decisions to address them. Faster Decision-Making Analyzing visual data leads to faster decision-making since the data has been simplified, with only the key aspects of it in focus when represented through visual mediums like graphs or reports. Quicker decision-making ensues, and action can be taken promptly based on the new insights. It can effectively boost business growth. Simplifying Complex Data A company deals with massive amounts of often cluttered and unorganized data, making it impossible to get actionable insights. One of the great benefits of big data visualization is that it helps businesses extract valuable insights from this vast amount of complex data. It helps them identify new patterns and detect errors, bringing their attention to bottlenecks, risks, or areas of progress. This entire process drives the businesses forward, leading them to success. While data scientists can uncover patterns without visualization, they must communicate data findings to the stakeholders by extracting critical information. One of the main benefits of data visualization is that it is interactive, which plays a key role in facilitating seamless communication. Example of Data Visualization A great example can be the use of data visualization during the COVID-19 pandemic. Data scientists retrieved data about the pandemic to gain insights into its spread, and visualization helped medical experts remain informed, communicate the key findings to the citizens and simplify the abundant information into understandable chunks. Purpose of Data Visualization Data visualization serves three main purposes.  Presenting data analysis results through data visualization enhances the impact of messaging to audiences by making it more persuasive. It also helps unify an organization’s messaging systems and fosters a shared understanding across various groups.  Using visualization can help you effortlessly understand large amounts of data and gain a clearer understanding of its impact on your business. It is an effective way to communicate insights to both internal and external audiences. It is important to consider the full picture by accessing accurate, unbiased data to make crucial business decisions. Combining data analysis and data visualization helps the business access relevant data that can drive business success. If they fail to consider this information, it can lead to costly mistakes. Earn data science courses from the World’s top Universities. Join our Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. What Next ? Long story short – data visualization is the key to success in the business world. If you leverage data visualization techniques, you are all set to reap the transformative power and benefits of Big Data. 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 Utkarsh Singh

11 Jun 2023

Blogs

6711

Hadoop is an open-source framework used for big data processes. It’s humongous and has many components. Each one of those components performs a specific set of big data jobs. Hadoop’s vast collection of solutions has made it an industry staple. And if you want to become a big data expert, you must get familiar with all of its components.  Don’t worry, however, because, in this article, we’ll take a look at all those components: Introduction to the Hadoop Ecosystem The Hadoop Ecosystem refers to a collection of open-source software tools and frameworks that work together to facilitate large-scale datasets storage, processing, and analysis. It offers a robust and scalable solution for handling big data challenges. The ecosystem comprises various components that address different aspects of data management and analysis. What are the Hadoop Core Components? Hadoop core components govern its performance and are you must learn about them before using other sections of its ecosystem. Hadoop’s ecosystem is vast and is filled with many tools. Another name for its core components is modules. There are primarily the following  Hadoop core components: HDFS The full form of HDFS is the Hadoop Distributed File System. It’s the most critical component of Hadoop as it pertains to data storage. HDFS lets you store data in a network of distributed storage devices. It has its set of tools that let you read this stored data and analyze it accordingly. HDFS enables you to perform acquisitions of your data irrespective of your computers’ operating system.  Read more about HDFS and it’s architecture. As you don’t need to worry about the operating system, you can work with higher productivity because you wouldn’t have to modify your system every time you encounter a new operating system. HDFS is made up of the following components: NameNode DataNode Secondary NameNode Name Node is also called ‘Master’ in HDFS. It stores the metadata of the slave nodes to keep track of data storage. It tells you what’s stored where. The master node also monitors the health of the slave nodes. It can assign tasks to data nodes, as well. Data nodes store the data. Data nodes are also called ‘Slave’ in HDFS. Slave nodes respond to the master node’s request for health status and inform it of their situation. In case a slave node doesn’t respond to the health status request of the master node, the master node will report it dead and assign its task to another data node.  Apart from the name node and the slave nodes, there’s a third one, Secondary Name Node. It is a buffer to the master node. It updates the data to the FinalFS image when the master node isn’t active.  MapReduce MapReduce is the second core component of Hadoop, and it can perform two tasks, Map and Reduce. Mapreduce is one of the top Hadoop tools that can make your big data journey easy. Mapping refers to reading the data present in a database and transferring it to a more accessible and functional format. Mapping enables the system to use the data for analysis by changing its form. Then comes Reduction, which is a mathematical function. It reduces the mapped data to a set of defined data for better analysis. It pars the key and value pairs and reduces them to tuples for functionality. MapReduce helps with many tasks in Hadoop, such as sorting the data and filtering of the data. Its two components work together and assist in the preparation of data. MapReduce also handles the monitoring and scheduling of jobs.  It acts as the Computer node of the Hadoop ecosystem. Mainly, MapReduce takes care of breaking down a big data task into a group of small tasks. You can run MapReduce jobs efficiently as you can use a variety of programming languages with it. It allows you to use Python, C++, and even Java for writing its applications. It is fast and scalable, which is why it’s a vital component of the Hadoop ecosystem.  Working of MapReduce MapReduce is a programming model and processing framework in Hadoop that enables distributed processing of large datasets. It consists of two main phases: the Map phase, where data is divided into key-value pairs, and the Reduce phase, where the results are aggregated. MapReduce efficiently handles parallel processing and fault tolerance, making it suitable for big data analysis. YARN YARN stands for Yet Another Resource Negotiator. It handles resource management in Hadoop. Resource management is also a crucial task. That’s why YARN is one of the essential Hadoop components. It monitors and manages the workloads in Hadoop. YARN is highly scalable and agile. It offers you advanced solutions for cluster utilization, which is another significant advantage. Learn more about Hadoop YARN architecture. YARN is made up of multiple components; the most important one among them is the Resource Manager. The resource manager provides flexible and generic frameworks to handle the resources in a Hadoop Cluster. Another name for the resource manager is Master. The node manager is another vital component in YARN. It monitors the status of the app manager and the container in YARN. All data processing takes place in the container, and the app manager manages this process if the container requires more resources to perform its data processing tasks, the app manager requests for the same from the resource manager.  Hadoop Common Apache has added many libraries and utilities in the Hadoop ecosystem you can use with its various modules. Hadoop Common enables a computer to join the Hadoop network without facing any problems of operating system compatibility or hardware. This component uses Java tools to let the platform store its data within the required system.  It gets the name Hadoop Common because it provides the system with standard functionality.  Hadoop Components According to Role Now that we’ve taken a look at Hadoop core components, let’s start discussing its other parts. As we mentioned earlier, Hadoop has a vast collection of tools, so we’ve divided them according to their roles in the Hadoop ecosystem. Let’s get started: Storage of Data Zookeeper Zookeeper helps you manage the naming conventions, configuration, synchronization, and other pieces of information of the Hadoop clusters. It is the open-source centralized server of the ecosystem.  HCatalog HCatalog stores data in the Binary format and handles Table Management in Hadoop. It enables users to use the data stored in the HIVE so they can use data processing tools for their tasks. It allows you to perform authentication based on Kerberos, and it helps in translating and interpreting the data. HDFS We’ve already discussed HDFS. HDFS stands for Hadoop Distributed File System and handles data storage in Hadoop. It supports horizontal and vertical scalability. It is fault-tolerant and has a replication factor that keeps copies of data in case you lose any of it due to some error.  Execution Engine Spark You’d use Spark for micro-batch processing in Hadoop. It can perform ETL and real-time data streaming. It is highly agile as it can support 80 high-level operators. It’s a cluster computing framework. Learn more about Apache spark applications. MapReduce This language-independent module lets you transform complex data into usable data for analysis. It performs mapping and reducing the data so you can perform a variety of operations on it, including sorting and filtering of the same. It allows you to perform data local processing as well.  Tez Tez enables you to perform multiple MapReduce tasks at the same time. It is a data processing framework that helps you perform data processing and batch processing. It can plan reconfiguration and can help you make effective decisions regarding data flow. It’s perfect for resource management. Database Management Impala You’d use Impala in Hadoop clusters. It can join itself with Hive’s meta store and share the required information with it. It is easy to learn the SQL interface and can query big data without much effort.  Hive The developer of this Hadoop component is Facebook. It uses HiveQL, which is quite similar to SQL and lets you perform data analysis, summarization, querying. Through indexing, Hive makes the task of data querying faster.  HBase HBase uses HDFS for storing data. It’s a column focused database. It allows NoSQL databases to create huge tables that could have hundreds of thousands (or even millions) of columns and rows. You should use HBase if you need a read or write access to datasets. Facebook uses HBase to run its message platform.  Solr and Lucene: Search and Indexing Capabilities Strong search and indexing tools like Apache Solr and Lucene are fully compatible with the Hadoop Ecosystem. Full-text search, faceted search, and rich document indexing are all possible with the aid of the search platform Solr. A Java package called Lucene offers more basic search functionality. The search and retrieval capabilities of Hadoop applications are improved by combining Solr and Lucene. Features of Solr and Lucene: Full-Text Search: Solr and Lucene provide powerful full-text search capabilities, allowing users to perform complex search queries on large volumes of text data. Scalability: Solr and Lucene can handle massive amounts of indexed data, making them suitable for enterprise-level search applications. Rich Document Indexing: Solr supports various document formats, including PDF, Word, and HTML, allowing users to index and search within documents. Faceted Search: Solr enables faceted search, allowing users to refine search results based on different categories or attributes. Oozie: Workflow Scheduler for Hadoop Oozie is a framework for Hadoop that manages workflow coordination and job scheduling. It enables users to build and control intricate workflows made up of several Hadoop tasks. Oozie allows extensibility through custom actions and supports a range of workflow control nodes. Users can oversee and automate the performance of data processing activities in a Hadoop cluster using Oozie. Features of Oozie: Workflow Orchestration: Oozie enables the definition and coordination of complex workflows with dependencies between multiple Hadoop jobs. Scheduling Capabilities: Oozie supports time-based and event-based triggers, allowing users to schedule and automate data processing tasks. Extensibility: Oozie allows the inclusion of custom actions, enabling the integration of external systems and tools into the workflow. Monitoring and Logging: Oozie provides monitoring and logging capabilities, allowing users to track the progress of workflows and diagnose issues. HCatalog: Metadata Management for Hadoop A central metadata repository is offered by the Hadoop table and storage management layer known as HCatalog. It makes data exchange easier between various Hadoop components and outside systems. With the help of HCatalog’s support for schema evolution, users can change the structure of stored data without affecting data access. It offers a uniform view of the data, which facilitates the management and analysis of datasets throughout the Hadoop Ecosystem. Features of HCatalog: Metadata Management: HCatalog stores and manages metadata, including table definitions, partitions, and schemas, allowing easy data discovery and integration. Schema Evolution: HCatalog supports schema evolution, allowing users to modify the data structure without impacting existing data or applications. Data Sharing: HCatalog facilitates sharing between different Hadoop components, enabling seamless data exchange and analysis. Integration: HCatalog integrates with external systems and tools, allowing data to be accessed and processed by non-Hadoop applications. Avro and Thrift: Data Serialization Formats Frameworks for data serialisation used in the Hadoop Ecosystem include Apache Avro and Thrift. They offer effective and language-neutral data serialization, simplifying data transfer across various platforms. Schema evolution is supported by Avro and Thrift, enabling schema evolution without compromising backward compatibility. Within the Hadoop Ecosystem, they are commonly utilized for data storage and exchange. Features of Avro and Thrift: Schema Evolution: Avro and Thrift support schema evolution, allowing for modifying data schemas without breaking compatibility with existing data. Language-Independent: Avro and Thrift provide language bindings for various programming languages, enabling data interchange between systems written in different languages. Compact Binary Format: Avro and Thrift use compact binary formats for efficient serialization and deserialization of data, reducing network overhead.Dynamic Typing: Avro and Thrift support dynamic typing, allowing flexibility in handling data with varying structures. Apache Drill Apache Drill lets you combine multiple data sets. It can support a variety of NoSQL databases, which is why it’s quite useful. It has high scalability, and it can easily help multitudes of users. It lets you perform all SQL-like analytics tasks with ease. It also has authentication solutions for maintaining end-to-end security within your system.  Abstraction Apache Sqoop You can use Apache Sqoop to import data from external sources into Hadoop’s data storage, such as HDFS or HBase. You can use it to export data from Hadoop’s data storage to external data stores as well. Sqoop’s ability to transfer data parallelly reduces excessive loads on the resources and lets you import or export the data with high efficiency. You can use Sqoop for copying data as well.  Apache Pig Developed by Yahoo, Apache pig helps you with the analysis of large data sets. It uses its language, Pig Latin, for performing the required tasks smoothly and efficiently. You can parallelize the structure of Pig programs if you need to handle humongous data sets, which makes Pig an outstanding solution for data analysis. Utilize our apache pig tutorial to understand more. Data Streaming Flume Flume lets you collect vast quantities of data. It’s a data collection solution that sends the collected data to HDFS. It has three sections, which are channels, sources, and finally, sinks. Flume has agents who run the dataflow. The data present in this flow is called events. Twitter uses Flume for the streaming of its tweets.  Kafka Apache Kafka is a durable, fast, and scalable solution for distributed public messaging. LinkedIn is behind the development of this powerful tool. It maintains large feeds of messages within a topic. Many enterprises use Kafka for data streaming. MailChimp, Airbnb, Spotify, and FourSquare are some of the prominent users of this powerful tool.  Learn more – Hadoop Components In this guide, we’ve tried to touch every Hadoop component briefly to make you familiar with it thoroughly. If you want to find out more about Hadoop components and its architecture, then we suggest heading onto our blog, which is full of useful data science articles.  If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data Programming. 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 Utkarsh Singh

09 Jun 2023

Blogs

899626

 Doug Cutting and Mike Cafarella created Hadoop way back in 2002. Hadoop originated from the Apache Nutch (an open-source web search engine) project, which was further a part of the Apache Lucene project. The goal was to design an open-source framework that allowed for data storing and processing in a distributed and automated computing environment. Hadoop is a software framework explicitly created for Big Data management, storage, and processing. It not only stores massive volumes of data, but it can also run applications on multiple clusters of commodity hardware.  Hadoop boasts of a highly scalable architecture, such that it can expand from a single server to hundreds and thousands of machines wherein each machine provides computation and storage. Its distributed feature enables speedy and seamless data transfer among the nodes in the cluster, thereby facilitating continued functioning even if a node fails.  Thanks to Hadoop’s distributed architecture, high scalability, high fault tolerance, enormous processing power, and fast processing speed, it is the perfect data management tool for businesses of all sizes. As a result, not only large corporations but also small and medium-sized businesses are adopting Hadoop. This growing adoption and demand for Hadoop services are creating a huge need for skilled Hadoop experts in the industry. Hadoop Developer is one of the many coveted Hadoop roles in demand right now. 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 Hadoop Developer? A Hadoop Developer specializes in handling and managing the requirements and processes associated with the Big Data domain. The job role is pretty similar to that of a Software Developer, with the only difference being that a Hadoop Developer focuses on Big Data. Hence, Hadoop Developers must possess in-depth knowledge of Hadoop tools and concepts, be familiar with all the elements of the Hadoop ecosystem (HDFS, YARN, and MapReduce), and understand the individual functioning of those elements as well as how they work together within the Hadoop ecosystem. Hadoop Developers are primarily responsible for designing, developing, implementing, and managing Big Data applications. The job of Hadoop Developers primarily revolves around Big Data. They collect data from disparate sources, clean and transform it, decode it to extract meaningful patterns, analyze it, and store it in a database for future use. They also prepare detailed visualization reports for the cleaned and transformed data using various Business Intelligence (BI) tools to help other stakeholders (particularly non-technical members) in the project understand the connotations of the extracted data. 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 Responsibilities of a Hadoop Developer To install, configure, and maintain the enterprise Hadoop environment. To source and collect data from multiple platforms in large volumes. To load data from different datasets and determine which is the best file format for a specific task.  To clean data to best fit the business requirements at hand using streaming APIs or user-defined functions. To build distributed, reliable, and scalable data pipelines for data ingestion and processing in real-time. To create and implement column family schemas of Hive and HBase within HDFS. To use different HDFS formats like Parquet, Avro, etc. to speed up system analytics. To understand the requirements of input to output transformations. To fine-tune Hadoop applications for improving their performance. To define Hadoop job flows. To review and manage Hadoop log files. To create Hive tables and assign schemas. To manage and deploy HBase clusters. To build new Hadoop clusters as and when needed. To troubleshoot and debug run time issues in the Hadoop ecosystem. 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 Skills required to become a Hadoop Developer Every Hadoop Developer must have the following skills: In-depth knowledge of the Hadoop ecosystem, its various components, along with different tools including HBase, Pig, Hive, Sqoop, Flume, Oozie, etc. In-depth knowledge of distributed systems. The ability to write precise, scalable, and high-performance code. Basic knowledge of scripting languages like Java, Python, and Perl. Basic knowledge of database structures and SQL. Excellent grasp over concurrency and multi-threading concepts. Experience in writing Pig Latin scripts and MapReduce jobs. Experience in data modeling with OLAP and OLTP. Experience in working with various data visualization tools like Qlikview and Tableau. Experience in working with ETL tools like Pentaho, Talend, Informatica, etc. Strong verbal and written communication skills. Analytical and problem-solving skills. Business acumen and domain knowledge. upGrad’s Exclusive Software Development Webinar for you – SAAS Business – What is So Different? document.createElement('video'); https://cdn.upgrad.com/blog/mausmi-ambastha.mp4   Also read: Data Scientist Salary in India How to become a Hadoop Developer? To become a Hadoop Developer, it is not mandatory to come from a Computer Science background – any related specialization such as Statistics/Mathematics/Data Analytics/Information Science will bode well for the job profile. After obtaining your graduate/postgraduate degree, the first step to becoming a Hadoop Developer would be to focus on acquiring the right skills for the job profile. So, keeping in mind the skills we’ve listed above, you must:  LearnJava, and SQL. Get familiar with Linux. Work with MapReduce algorithms. Learn different database concepts. Learn the nitty-gritty of Hadoop ecosystem Learn different Hadoop and HDFS commands. Start writing beginner-level code for Hadoop. Dig deeper into Hadoop programming.  Take up production-grade Hadoop projects. Apart from these steps, here are some tips that will help you become a good Hadoop Developer: Own the data – Since the job requires you to spend a great deal of time in collecting, cleaning, and transforming the data for further analysis and storage, you must dig deep into the data you are working with. This will help you to gain the optimum beneficial insights from the data.  Be ready to learn new things – You should always be open to learning new concepts and new technologies that could help you improve your Hadoop projects and applications. Focus on learning Data Science techniques – Invest your time to learn about the different Data Science techniques such as data mining, data transformation, data visualization, among other things. This will help you to use the data to its maximum potential to solve diverse business challenges. Hadoop Developer Salary in India Hadoop Developers can find job opportunities across various sectors of the industry, including IT, finance, healthcare, retail, manufacturing, advertising, telecommunications, media & entertainment, travel, hospitality, transportation, and even in government agencies. However, the six major industries that are driving the demand for Hadoop talent in India are IT, e-commerce, retail, manufacturing, insurance, and finance. Of all the industries, e-commerce records as having the highest Hadoop salaries in India. From big names like Amazon, Netflix, Google, and Microsoft to startups like Fractal Analytics, Sigmoid Analytics, and Crayon Data – every company is investing in Big Data and Hadoop talent.  The Hadoop Developer salary in India mainly depends upon a candidate’s educational qualifications, skill set, work experience, and the company size and reputation, and job location. For instance, candidates who have a postgraduate degree can earn a starting package of around Rs. 4 – 8 LPA. However, graduate freshers can earn between Rs. 2.5 – 3.8 LPA. Similarly, professionals who possess the best combination of the skills we’ve mentioned above can earn anywhere between Rs. 5 – 10 LPA. Mid-level professionals in a non-managerial capacity receive an average annual package of Rs. 7 – 15 LPA and those in managerial roles can make around Rs. 12 -18 LPA or more. The salary scale of senior-level Hadoop Developers (with over 15 years of experience) is usually very high, ranging between Rs. 28 – 50 LPA or more.  The global Hadoop Big Data market is projected to grow from US$ 4.91 billion in 2015 to US$ 40.69 billion by 2021, recording a CAGR (Compound Annual Growth Rate) of 43.4% during the forecast period. This indicates positive growth in the demand for Hadoop Developers in the years to come.  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? Job roles for Hadoop Developers: The knowledge of different job roles related to Hadoop developers can help you to determine which one to choose. 1.Hadoop Software Engineer A Hadoop software engineer can work with a software development team that works on the company’s current projects. Some of the key duties of this job role include developing computer code validation and testing tactics and working on software programming. These engineers work closely with shoppers and other departments to convey project tenders and statuses.  2. Hadoop Senior Software Engineer They are proficient at working on the latest software technologies capable of solving business concerns. The term “senior” means that they possess big data skills using Storm/Hadoop and ML algorithms to solve business issues. Moreover, this category of Hadoop developer possesses an in-depth understanding of distributed systems and is an expert at using corresponding frameworks to make applications more powerful. 3. Hadoop Software Developer They look after Hadoop applications’ programming. Some of their job duties resemble that of software system developers. They are also proficient at developing Hadoop applications and systems. They must be acquainted with the big data fundamentals to perform their job duties flawlessly.  Furthermore, they know data manipulation, storage, amendments, and decoding. 4. Data Engineer They optimize data and the data pipeline-based design. They are also proficient at data pipeline building and data wrangling for building data systems and optimizing them. They can indirectly assist software system developers, data analysts, info architects, and data scientists. They assure outstanding data pipeline design when they work with these professionals. This job role of a Hadoop developer demands that professionals must be independent and comfortable when fulfilling the needs of multiple systems and groups. Moreover, they are proficient at redesigning the business’ data design to facilitate cutting-edge data and products. List of Companies hiring for the position of Hadoop jobs in India Cognizant Infosys Amazon Alteryx Ayata Flipkart IBM United Health Group TCS   Benefits of learning Hadoop 1) Data safety: Hadoop’s excellent fault tolerance ability makes it a suitable choice for large-scale companies looking to protect their data. It provides high-level protection for single and multiple data failures. Hadoop’s internal working implies that the data is conveyed to individual nodes wherein the data replicates to other nodes. You can expect a high Hadoop admin salary in India if you are proficient at ensuring the organization’s data safety. 2) Affordability: The business’ datasets tend to increase with time. Hadoop offers an effective solution for the proper storage of voluminous data. The use of conventional RDBMS proves to be expensive for organizations to scale up their data. Thus, Hadoop offers an affordable solution for data scalability. When using those conventional systems, organizations occasionally have to restrict their data, but this issue is not found when using Hadoop. It can store approx. hundreds of pounds for every Terabyte. So, it is useful as an authentic data storage solution for the voluminous data intended for future use. A decent big data Hadoop salary is guaranteed if the developers can proficiently explore all the benefits of Hadoop. 3) Scalability: Implied from the name itself, it indicates the capability to manage massive data for growth purposes. Hadoop is one of the greatest scalable platforms when it comes to data storage. This is because it has the potential to disburse massive datasets among various parallel servers. The conventional RDBMSs can’t scale huge volumes of data. Conversely, Hadoop can work on a myriad of nodes. The Hadoop admin salary in India is inclusive of how skilfully the developers can scale the data. 4) Quick operation: The data present on the Hadoop system is allocated on a file system within a cluster called ‘Maps’. One of the unique features of Hadoop is the distributed file system. Hadoop facilitates the quick processing of data via the same servers that process the data. Moreover, it can process unstructured data at a speed of a few terabytes within a few minutes. 5) Versatility: Hadoop supports structured as well as unstructured data. So, it facilitated the organizations to provide hassle-free access to different data sources. This is possible by simply switching among different data types. You can use Hadoop to deliver valued business insights from varied sources such as social media platforms, emails, clickstream data, etc. Hadoop is also useful in log processing, data warehousing, market campaign investigation, fraud detection, and recommendation systems. So, the versatility of Hadoop suggests the outstanding Hadoop admin salary in India for skilled candidates. 6) Wide range of applications: Hadoop provides topmost priority to data, and so it deters data loss. It makes the most of the data. Its architecture involves creating comprehensive sets of data rather than developing data samples for analysis. The comprehensive datasets lead to in-depth data analysis and provide optimal solutions. One of the reasons why many companies are happy to offer high big data Hadoop salary is that the developers can work on various types of applications. 7) Outstanding career opportunities: Considering the huge share of organizations actively working with big data, Hadoop will have a considerable share in job opportunities in the future. The developers must have exceptional skills for data harnessing. So, Hadoop looks after framing cost-effective plans. In such cases, there will be more chances of obtaining a handsome big data Hadoop salary. Conclusion We hope you liked our article on Hadoop developer 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. 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 Utkarsh Singh

21 Nov 2022

Blogs

6521

Impala is an open-source, native analytic database designed for clustered platforms like Apache Hadoop. It is an interactive SQL-like query engine that runs on top of the Hadoop Distributed File System (HDFS) to facilitate the processing of massive volumes of data at a lightning-fast speed. Also, impala is one of the top Hadoop tools to use big data. Today, we’re going to talk about all things Impala, and hence, we’ve designed this Impala tutorial for you! This Impala Hadoop tutorial is specially intended for those who wish to learn Impala. However, to reap the maximum benefits of this Impala tutorial, it would help if you have an in-depth understanding of the fundamentals of SQL along with Hadoop and HDFS commands.  What is Impala? Impala is an MPP (Massive Parallel Processing) SQL query engine written in C++ and Java. Its primary purpose is to process vast volumes of data stored in Hadoop clusters. Impala promises high performance and low latency, and it is to date the top-performing SQL engine (that offers an RDBMS-like experience) to provide the fastest way to access and process data stored in HDFS. Another beneficial aspect of Impala is that it integrates with the Hive metastore to allow sharing of the table information between both components. It leverages the existing Apache Hive to perform batch-oriented, long-running jobs in SQL query format. The Impala-Hive integration allows you to use either of the two components – Hive or Impala for data processing or to create tables under a single shared file system (HDFS) without altering the table definition. Why Impala? Impala combines the multi-user performance of a traditional analytic database and SQL support with the scalability and flexibility of Apache Hadoop. It does so by using standard Hadoop components like HDFS, HBase, YARN, Sentry, and Metastore. Since Impala uses the same metadata, user interface (Hue Beeswax), SQL syntax (Hive SQL), and ODBC (Open Database Connectivity) driver as Apache Hive, it creates a unified and familiar platform for batch-oriented and real-time queries.  Read: Big Data Project Ideas for Beginners Impala can read almost all the file formats used by Hadoop, including Parquet, Avro, and RCFile. Also, Impala is not built on MapReduce algorithms – it implements a distributed architecture based on daemon processes that handle and manage everything related to query execution running on the same machine/s. As a result, it helps reduce the latency of utilizing MapReduce. This is precisely what makes Impala much faster than Hive.  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 Impala – Features The main features of Impala are: It is available as an open-source SQL query engine under the Apache license. It lets you access data by using SQL-like queries. It supports in-memory data processing – it accesses and analyzes data stored on Hadoop data nodes. It allows you to store data in storage systems like HDFS, Apache HBase, and Amazon s3. It easily integrates with BI tools like Tableau, Pentaho, and Micro strategy. It supports various file formats including Sequence File, Avro, LZO, RCFile, and Parquet. Impala – Key Advantages Using Impala offers some significant advantages to the users, like: Since Impala supports in-memory data processing (processing occurs where the data resides – on Hadoop cluster), there’s no need for data transformation and data movement. To access data stored in HDFS, or HBase, or Amazon s3 with Impala, you do not need any prior knowledge of Java (MapReduce jobs) – you can easily access it using basic SQL queries. Generally, data has to undergo a complicated extract-transform-load (ETL) cycle while writing queries in business tools. However, with Impala, there’s no need for this. Impala replaces the time-consuming stages of loading & re-organizing with advanced techniques like exploratory data analysis & data discovery, thereby boosting the speed of the process. Impala is a pioneer for using the Parquet file format, which is a columnar storage layout optimized for large-scale queries found in data warehouses. Impala – Drawbacks Although Impala offers numerous benefits, it has certain limitations as well: It has no support for serialization and deserialization. It cannot read custom binary files; it can only read text files. Every time new records or files are added to the data directory in HDFS, you will need to refresh the data table. 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 Impala – Architecture Impala is decoupled from its storage engine (contrary to traditional storage systems). It includes three principal components – Impala Daemon (Impalad), Impala StateStore, and Impala Metadata & MetaStore.   Impala Daemon Impala Daemon, a.k.a. Impalad runs on individual nodes where Impala is installed. It accepts queries from multiple interfaces (Impala shell, Hue browser, etc.) and processes them. Each time a query is submitted to an Impalad on a particular node, the node becomes a “coordinator node” for that query. In this way, multiple queries are served by Impalad running on other nodes. Once the queries are accepted, Impalad reads and writes data files and parallelizes the queries by distributing the task to the other Impala nodes in the cluster. Users can either submit queries to a dedicated Impalad or in a load-balanced manner to other Impalad in the cluster, based on their requirements. These queries then start processing on the different Impalad instances and return the result to the primary coordinating node. Impala StateStore The Impala StateStore monitors and checks the health of each Impalad and also relays the health report of each Impala Daemon health to the other daemons. It can run on the same node where the Impala server is running or in another node in the cluster. In case there’s a node failure due to some reason, the Impala StateStore updates all the other nodes about the failure. In such an event, the other Impala daemons stop assigning any further queries to the failed node. Impala Metadata & MetaStore In Impala, all the crucial information, including table definitions, table and column information, etc., are stored within a centralized database known as the MetaStore. When dealing with substantial volumes of data containing multiple partitions, it becomes challenging to obtain table-specific metadata. This is where Impala comes to the rescue. Since individual Impala nodes cache all the metadata locally, it becomes easy to obtain specific information instantly.  Each time you update the table definition/table data, all Impala Daemons must also update their metadata cache by retrieving the latest metadata before they can issue a new query against a particular table. 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 Impala – Installing Impala Just like you need to install Hadoop and its ecosystem on Linux OS, you can do the same with Impala. Since it was Cloudera that first shipped Impala, you can easily access it via the Cloudera QuickStart VM. Read: Hadoop Tutorial How to download the Cloudera QuickStart VM To download the Cloudera QuickStart VM, you must follow the steps outlined below. Step 1 Open the Cloudera homepage (http://www.cloudera.com/), and you will find something like this: Step 2 To register on Cloudera, you must click the “Register Now” option, which will open the Account Registration page. If you are already registered on Cloudera, you can click on the “Sign In” option on the page, and it will further redirect you to the sign-in page like so: Step 3 Once you’ve signed in, open the download page of the website by clicking on the “Downloads” option at the top left corner of the page, as shown below: Step 4 In this step, you need to download the Cloudera QuickStartVM by clicking on the “Download Now” option like so: Clicking on the Download Now option will redirect you to the download page of QuickStart VM: Then you have to select the GET ONE NOW option, accept the license agreement, and submit it as shown below: After the download is complete, you will find three different Cloudera VM Compatible options – VMware, KVM, and VIRTUALBOX. You can choose your preferred option. Source Impala – Query Processing Interfaces Impala offers three interfaces for processing queries: Impala-shell – Once you’ve installed and set up Impala using the Cloudera VM, you can activate Impala-shell by typing the command “impala-shell” in the editor.  Read: Difference Between Big Data & Hadoop Hue interface – The Hue browser allows you to process Impala queries. It has an Impala query editor where you can type and execute different Impala queries. However, to use the editor, first, you will need to log in to the Hue browser. ODBC/JDBC drivers – As is true of every database, Impala also offers ODBC/JDBC drivers. These drivers let you connect to Impala through programming languages that support them (ODBC/JDBC drivers) and build applications that process queries in Impala using the same programming languages. Query Execution Procedure Whenever you pass a query using any Impala interfaces, an Impalad in the cluster usually accepts your query. This Impalad then becomes the coordinator node for that particular query. After receiving the query, the coordinator verifies whether or not the query is appropriate by using the Table Schema from the Hive Metastore. After this, it gathers information about the location of the data that is needed for the query execution from the HDFS name node and forwards this information to other Impalads in the hierarchy to facilitate query execution. Once the Impalads read the specified data block, they process the query. When ll the Impalads in the cluster have processed the query, the coordinator node collects the result and delivers it to you. Impala Shell Commands If you are familiar with Hive Shell, you can easily figure out Impala Shell since both share a pretty similar structure – they allow to create databases and tables, insert data, and issue queries. Impala Shell commands fall under three broad categories: general commands, query specific options, and table and database-specific options. General Commands help  The help command offers a list of useful commands available in Impala. [quickstart.cloudera:21000] > help;  Documented commands (type help <topic>): ======================================================== compute  describe insert  set unset with  version connect  explain quit    show values use exit     history profile select  shell tip  Undocumented commands: =========================================  alter create desc drop help load summary Version This command provides you with the current version of Impala. [quickstart.cloudera:21000] > version; Shell version: Impala Shell v2.3.0-cdh5.5.0 (0c891d7) built on Mon Nov 9  12:18:12 PST 2015 Server version: impalad version 2.3.0-cdh5.5.0 RELEASE (build  0c891d79aa38f297d244855a32f1e17280e2129b) history This command displays the last ten commands executed in Impala Shell.  [quickstart.cloudera:21000] > history; [1]:version; [2]:help; [3]:show databases; [4]:use my_db; [5]:history; connect This command helps connect to a given instance of Impala. If you do not specify any instance, then by default, it will connect to the default port 21000. [quickstart.cloudera:21000] > connect;  Connected to quickstart.cloudera:21000  Server version: impalad version 2.3.0-cdh5.5.0 RELEASE (build  0c891d79aa38f297d244855a32f1e17280e2129b) exit/quit  As the name suggests, the exit/quit command lets you exit the Impala Shell. [quickstart.cloudera:21000] > exit;  Goodbye cloudera Query Specific Options explain This command returns the execution plan for a particular query. [quickstart.cloudera:21000] > explain select * from sample; Query: explain select * from sample +————————————————————————————+  | Explain String                                                                     |  +————————————————————————————+  | Estimated Per-Host Requirements: Memory = 48.00MB VCores = 1                       |  | WARNING: The following tables are missing relevant table and/or column statistics. | | my_db.customers                                                                                                          |  | 01:EXCHANGE [UNPARTITIONED]                                                                    |  | 00:SCAN HDFS [my_db.customers]                                                                            |  | partitions = 1/1 files = 6 size = 148B                                                                              |  +————————————————————————————+  Fetched 7 row(s) in 0.17s profile This command displays the low-level information about the recent/latest query. It is used for diagnosis and performance tuning of a query. [quickstart.cloudera:21000] > profile; Query Runtime Profile:  Query (id=164b1294a1049189:a67598a6699e3ab6):     Summary:        Session ID: e74927207cd752b5:65ca61e630ad3ad       Session Type: BEESWAX        Start Time: 2016–04–17 23:49:26.08148000 End Time: 2016–04–17 23:49:26.2404000        Query Type: EXPLAIN        Query State: FINISHED        Query Status: OK        Impala Version: impalad version 2.3.0–cdh5.5.0 RELEASE (build 0c891d77280e2129b)        User: cloudera        Connected User: cloudera        Delegated User:        Network Address:10.0.2.15:43870        Default Db: my_db        Sql Statement: explain select * from sample        Coordinator: quickstart.cloudera:22000        : 0ns        Query Timeline: 167.304ms           – Start execution: 41.292us (41.292us) – Planning finished: 56.42ms (56.386ms)           – Rows available: 58.247ms (1.819ms)           – First row fetched: 160.72ms (101.824ms)           – Unregister query: 166.325ms (6.253ms)          ImpalaServer:        – ClientFetchWaitTimer: 107.969ms        – RowMaterializationTimer: 0ns  Table and Database Specific Options alter The alter command helps to change the structure and name of a table. describe The describe command provides the metadata of a table. It contains information like columns and their data types. drop The drop command helps to remove a construct, which can be a table, a view, or a database function. insert The insert command helps to append data (columns) into a table and override the data of an existing table select The select command can be used to perform a specific operation on a particular dataset. It usually mentions the dataset on which the action is to be completed. show The show command displays the metastore of various constructs like tables and databases. use The use command helps to change the current context of a particular database. Impala – Comments In Impala, the comments are similar to those in the SQL language. Typically, there are two types of comments: Single-line comments Each line that is followed by “—” becomes a comment in Impala. — Hello, welcome to upGrad. Multiline comments All the lines contained between /* and */ are multiline comments in Impala. /* Hi this is an example Of multiline comments in Impala */ Conclusion We hope that this detailed Impala tutorial helped you understand its intricacies and how it functions.  If you are interested to know more about Big Data, check out our PG Diploma in Software Development Specialization in Big Data program which is designed for working professionals and provides 7+ case studies & projects, covers 14 programming languages & tools, practical hands-on workshops, more than 400 hours of rigorous learning & job placement assistance with top firms. 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 Utkarsh Singh

13 Oct 2022

Blogs

1043

Ever since Big Data took the tech and business worlds by storm, there’s been an enormous upsurge of Big Data tools and platforms, particularly of Apache Hadoop and Apache Spark. Today, we’re going to focus solely on Apache Spark and discuss at length about its business benefits and applications.  Apache Spark came to the limelight in 2009, and ever since, it has gradually carved out a niche for itself in the industry. According to Apache org., Spark is a “lightning-fast unified analytics engine” designed for processing colossal amounts of Big Data. Thanks to an active community, today, Spark is one of the largest open-source Big Data platforms in the world.   Check out our free courses to get an edge over the competition. 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 What is Apache Spark? Originally developed in the University of California’s (Berkeley) AMPLab, Spark was designed as a robust processing engine for Hadoop data, with a special focus on speed and ease of use. It is an open-source alternative to Hadoop’s MapReduce. Essentially, Spark is a parallel data processing framework that can collaborate with Apache Hadoop to facilitate the smooth and fast development of sophisticated Big Data applications on Hadoop.  Spark comes packed with a wide range of libraries for Machine Learning (ML) algorithms and graph algorithms. Not just that, it also supports real-time streaming and SQL apps via Spark Streaming and Shark, respectively. The best part about using Spark is that you can write Spark apps in Java, Scala, or even Python, and these apps will run nearly ten times faster (on disk) and 100 times faster (in memory) than MapReduce apps. Apache Spark is quite versatile as it can be deployed in many ways, and it also offers native bindings for Java, Scala, Python, and R programming languages. It supports SQL, graph processing, data streaming, and Machine Learning. This is why Spark is widely used across various sectors of the industry, including banks, telecommunication companies, game development firms, government agencies, and of course, in all the top companies of the tech world – Apple, Facebook, IBM, and Microsoft. 6 Best Features of Apache Spark The features that make Spark one of the most extensively used Big Data platforms are: 1. Lighting-fast processing speed Big Data processing is all about processing large volumes of complex data. Hence, when it comes to Big Data processing, organizations and enterprises want such frameworks that can process massive amounts of data at high speed. As we mentioned earlier, Spark apps can run up to 100x faster in memory and 10x faster on disk in Hadoop clusters. It relies on Resilient Distributed Dataset (RDD) that allows Spark to transparently store data on memory and read/write it to disc only if needed. This helps to reduce most of the disc read and write time during data processing. 2. Ease of use Spark allows you to write scalable applications in Java, Scala, Python, and R. So, developers get the scope to create and run Spark applications in their preferred programming languages. Moreover, Spark is equipped with a built-in set of over 80 high-level operators. You can use Spark interactively to query data from Scala, Python, R, and SQL shells. 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 3. It offers support for sophisticated analytics Not only does Spark support simple “map” and “reduce” operations, but it also supports SQL queries, streaming data, and advanced analytics, including ML and graph algorithms. It comes with a powerful stack of libraries such as SQL & DataFrames and MLlib (for ML), GraphX, and Spark Streaming. What’s fascinating is that Spark lets you combine the capabilities of all these libraries within a single workflow/application. 4. Real-time stream processing Spark is designed to handle real-time data streaming. While MapReduce is built to handle and process the data that is already stored in Hadoop clusters, Spark can do both and also manipulate data in real-time via Spark Streaming. Unlike other streaming solutions, Spark Streaming can recover the lost work and deliver the exact semantics out-of-the-box without requiring extra code or configuration. Plus, it also lets you reuse the same code for batch and stream processing and even for joining streaming data to historical data. 5. It is flexible Spark can run independently in cluster mode, and it can also run on Hadoop YARN, Apache Mesos, Kubernetes, and even in the cloud. Furthermore, it can access diverse data sources. For instance, Spark can run on the YARN cluster manager and read any existing Hadoop data. It can read from any Hadoop data sources like HBase, HDFS, Hive, and Cassandra. This aspect of Spark makes it an ideal tool for migrating pure Hadoop applications, provided the apps’ use-case is Spark-friendly. 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The anatomy of Spark Applications Every Spark application comprises of two core processes – a primary driver process and a collection of executor processes.  Source  The driver process that sits on a node in the cluster is responsible for running the main() function. It also handles three other tasks – maintaining information about the Spark Application, responding to a user’s code or input, and analyzing, distributing, and scheduling work across the executors. The driver process forms the heart of a Spark Application – it contains and maintains all critical information covering the lifetime of the Spark application. The executors or executor processes are secondary items that must execute the task assigned to them by the driver. Basically, each executor performs two crucial functions – run the code assigned to it by the driver and report the state of the computation (on that executor) to the driver node. Users can decide and configure how many executors each node should have. In a Spark application, the cluster manager controls all machines and allocates resources to the application. Here, the cluster manager can be any one of Spark’s core cluster managers, including YARN (Spark’s standalone cluster manager) or Mesos. This entails that a cluster can run multiple Spark Applications simultaneously. Real-world Apache Spark Applications  Spark is a top-rated and widely used Big Dara platform in the modern industry. Some of the most acclaimed real-world examples of Apache Spark applications are: Spark for Machine Learning Apache Spark boasts of a scalable Machine Learning library – MLlib. This library is explicitly designed for simplicity, scalability, and facilitating seamless integration with other tools. MLlib not only possesses the scalability, language compatibility, and speed of Spark, but it can also perform a host of advanced analytics tasks like classification, clustering, dimensionality reduction. Thanks to MLlib, Spark can be used for predictive analysis, sentiment analysis, customer segmentation, and predictive intelligence. Another impressive feature of Apache Spark rests in the network security domain. Spark Streaming allows users to monitor data packets in real time before pushing them to storage. During this process, it can successfully identify any suspicious or malicious activities that arise from known sources of threat. Even after the data packets are sent to the storage, Spark uses MLlib to analyze the data further and identify potential risks to the network. This feature can also be used for fraud and event detection.  Spark for Fog Computing Apache Spark is an excellent tool for fog computing, particularly when it concerns the Internet of Things (IoT). The IoT heavily relies on the concept of large-scale parallel processing. Since the IoT network is made of thousands and millions of connected devices, the data generated by this network each second is beyond comprehension. Naturally, to process such large volumes of data produced by IoT devices, you require a scalable platform that supports parallel processing. And what better than Spark’s robust architecture and fog computing capabilities to handle such vast amounts of data! Fog computing decentralizes the data and storage, and instead of using cloud processing, it performs the data processing function on the edge of the network (mainly embedded in the IoT devices). To do this, fog computing requires three capabilities, namely, low latency, parallel processing of ML, and complex graph analytics algorithms – each of which is present in Spark. Furthermore, the presence of Spark Streaming, Shark (an interactive query tool that can function in real-time), MLlib, and GraphX (a graph analytics engine) further enhances Spark’s fog computing ability.  Spark for Interactive Analysis Unlike MapReduce, or Hive, or Pig, that have relatively low processing speed, Spark can boast of high-speed interactive analytics. It is capable of handling exploratory queries without requiring sampling of the data. Also, Spark is compatible with almost all the popular development languages, including R, Python, SQL, Java, and Scala. The latest version of Spark – Spark 2.0 – features a new functionality known as Structured Streaming. With this feature, users can run structured and interactive queries against streaming data in real-time. Check our other Software Engineering Courses at upGrad. Users of Spark Now that you are well aware of the features and abilities of Spark, let’s talk about the four prominent users of Spark! 1. Yahoo Yahoo uses Spark for two of its projects, one for personalizing news pages for visitors and the other for running analytics for advertising. To customize news pages, Yahoo makes use of advanced ML algorithms running on Spark to understand the interests, preferences, and needs of individual users and categorize the stories accordingly. For the second use case, Yahoo leverages Hive on Spark’s interactive capability (to integrate with any tool that plugs into Hive) to view and query the advertising analytic data of Yahoo gathered on Hadoop.  2. Uber  Uber uses Spark Streaming in combination with Kafka and HDFS to ETL (extract, transform, and load) vast amounts of real-time data of discrete events into structured and usable data for further analysis. This data helps Uber to devise improved solutions for the customers. 3. Conviva As a video streaming company, Conviva obtains an average of over 4 million video feeds each month, which leads to massive customer churn. This challenge is further aggravated by the problem of managing live video traffic. To combat these challenges effectively, Conviva uses Spark Streaming to learn network conditions in real-time and to optimize its video traffic accordingly. This allows Conviva to provide a consistent and high-quality viewing experience to the users. 4. Pinterest On Pinterest, users can pin their favourite topics as and when they please while surfing the Web and social media. To offer a personalized and enhanced customer experience, Pinterest makes use of Spark’s ETL capabilities to identify the unique needs and interests of individual users and provide relevant recommendations to them on Pinterest. 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 To conclude, Spark is an extremely versatile Big Data platform with features that are built to impress. Since it an open-source framework, it is continuously improving and evolving, with new features and functionalities being added to it. As the applications of Big Data become more diverse and expansive, so will the use cases of Apache Spark. If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

by Utkarsh Singh

06 Oct 2022