Hadoop Clusters Overview: Benefits, Architecture & Components

Looking to upskill IT professionals for a $100 billion opportunity in Data and Digital, BITS Pilani has launched a new program in Big Data Engineering, in association with UpGrad. As per recent industry estimates, radical technology changes and increasing automation is expected to lead to an elimination of almost 20-30% jobs in the Indian IT sector, amounting to over 1 million layoffs. Most of these jobs need to be repositioned to avoid a net loss of jobs in this sector. New age technologies in digital and data, which are re-defining several existing roles. It represents an estimated $100 billion revenue opportunity for the IT industry and can potentially create 1.5-2 million additional jobs in the sector, by 2025. The most important task ahead, for the young professionals working in the IT and allied sectors, and who form a large part of India’s consumption story and its middle class, is to re-skill while working. The rapid changes occurring across industries and businesses are likely to affect them the most. 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   For these professionals, online education presents a valuable option to stay relevant without quitting their jobs. Recognizing the needs of these professionals and the Industry, BITS Pilani has launched an online Post-Graduate Program in Big Data Engineering, in association with UpGrad. The program will train students in areas like Batch Processing, Real-Time Data Processing, and Big Data Analytics. Recent industry estimates expect Big Data & Analytics to grow at a 26% CAGR to $16 billion by 2025 – creating a need for almost a million data engineers. Prof. Sundar (Director – Off-Campus Programmes & Industry Engagement, BITS Pilani) says, “Big Data is increasingly finding adoption in all critical business applications. For this domain to realize its full potential, there is a need for high-quality technical talent in large numbers.” On the other hand, online education is widely gaining acceptance. “In the last couple of years, online as a platform has matured. It has the potential to provide a transformative learning experience to professionals in India, at a large-scale. Through this program with BITS Pilani, we hope to empower many individuals to meet their full professional potential,” added Ronnie Screwvala and Mayank Kumar, Co-founders of UpGrad. Speaking on the partnership with UpGrad, Prof. Gurunarayanan (Dean – Work Integrated Learning Programmes, BITS Pilani) mentioned, “BITS Pilani has a long history of providing quality technical education. The prospect of combining our subject matter expertise with UpGrad’s ability to deliver quality online learning experience to a large number of students is very exciting.” 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 If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. 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 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. 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 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?

With the rapid advancement of Big Data, its power and influence are increasing very rapidly. Likewise, technologies, applications, and opinions based on Big Data are swiftly rising. Big Data may be the next big thing or utterly dead; a panacea or menace; the key to all future innovation or just a hollow branding term. Between these extremes, Big Data is an important area of focus for consumer finance. It has the potential to support and scale consumer financial health. Big Data’s Evolution in Consumer Finance Big data is a set of tools that can be used for creating, refining, and scaling financial solutions. It is sewn into the consumer financial services marketplace, in sophisticated ways. It is instructive to examine the greatest potential areas for the further development of big data. Also, the ways to foster its use in a safe, responsible, and beneficial manner on a large scale. Big data is now a fundamental element of risk-profiling for the banks. Analysts can study the impact of geopolitical escalations on different market segments. Now, banks can map out market-shaping events in the past to predict future patterns. Investment banks are using big data to analyse the effectiveness of their deals. They do this by studying the insights of trades they did or did not win on a client-by-client basis. The data systems at most banks are not like retail giants or startups or fin-tech companies. They were not constructed to analyse structured and unstructured data. Remodeling the entire IT and data systems needed a deep analysis of a bank’s data. Updating is very time-consuming and costly. Some banks have merged or acquired other banks or financial services businesses. These are facing even more complex issues while incorporating and updating IT systems. This is where big data can prove to be a game changer. 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 Surge in hiring of big data analytics specialists The competition between banks and fund managers to hire big data specialists is heating up. Banks are actively recruiting to fill two main, but different roles: Big Data Engineers and Data Scientists/Analyst. Big Data Engineers are coming from a strong IT background. They have development or coding experience and are responsible for designing data platforms and applications. Data Scientists, in contrast, are bridging the gap between data analytics and business decision making. They’re capable of translating complex data into key strategic insight. Data scientists are also known as analytics and insights manager or director of data science. They should have sharp technical and quantitative skills. 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 Organisations working with Big Data, like Investment Banks usually follow this hierarchical structure: Junior Associate – A big data developer mainly working on Hadoop, Spark, Sqoop, Pig, Hive, HDFS, HBase. They’d have 5-6 years of industry experience in basic Java/Python/Scala programming. Salary Range: INR 12-18 Lakhs per annum Senior Associate – A big data senior developer working on Hadoop, Spark, Sqoop, Pig, Hive, HDFS, HBase. They’d have an industry experience of 7 to 10 years in advanced Java/Python/Scala programming. Salary Range: INR 18-25 Lakhs per annum Vice President – A big data architect with architecture experience in Hadoop, Spark, Hive, Pig, Sqoop, HDFS, HBase. They’d have expert programming knowledge in Java/Python/Scala with 10 to 15 years of experience. Salary Range: INR 25-50 Lakhs per annum The salaries of Big Data Engineers/Architects are 15-20% higher than other technologies in the current market scenario. Combining massive data sets thoughtfully can lead to greater accuracy and granularity. Financially underserved consumers often have unique combinations of needs. Thus, tools allowing scalable tailored services at low costs are vital to the mutual success of consumers and providers. However, the Big Data mosaic effect has also often raised concerns about its potential risk to consumer privacy, combining large data results in overly sensitive insights. From my experience, a career in Big Data is extremely rewarding in the present scenario, especially in the financial sector. Huge volumes of data are threatening technologies like data warehousing. I have shifted in my own career from being a data warehouse architect into big data and data science as that is the need of the hour. What do you think will be the impact of Big Data and other data technologies in the near future? Comment below and let us know. 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 Conclusion 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. 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?

Do you ever wonder how Aadhaar data belonging to more than 1.32 billion Indian citizens is stored? How the generation of one million Aadhaar numbers is achieved by performing 600 trillion matches in a day? Have you ever wondered how 100 million authentications are undertaken; establishing the identity of a person by UIDAI in a day? This article aims to provide answers to these questions. Along the way, this article will enumerate the requirement of Aadhaar and the two essential tasks of the UIDAI, i.e. enrollment and authentication. UIDAI has leveraged big data technologies like open scale-out, open-source, cheap commodity hardware, distributed computing technologies, etc. in handling and processing vast amounts of data. Aadhaar a necessity? The Indian Government was spending about 25 to 40 billion dollars on direct subsidies. According to CIA World Factbook, the GDP of North Korea was 40 billion for the year 2014. We are spending the equivalent of North Korea’s GDP on direct subsidies. The problem is not the subsidy, but the leakage of it. Most programs suffered due to ghost and multiple identities. Indians didn’t have any standard identity document. We possess many certificates viz., driving license, PAN card, voter card, etc. issued by central and state government authorities. All these certificates/cards were domain restricted. It was difficult to establish the identity of a person with these cards issued by the government. So, there was a need felt for a document which could uniquely determine the identity of a person. Thus, one of the most challenging projects ever took birth. The task of providing identification to one billion people, i.e. one-sixth of the world’s population. 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 Roles and Salaries in the Finance Industry Tasks performed by UIDAI Two critical tasks performed by the UIDAI are enrollment and authentication. Enrollment is the process of providing a new Aadhaar number to a citizen. Authentication is the process of establishing the identity of a person. Both are entirely different beasts with their peculiar challenges. Enrollment is an asynchronous process. An Aadhaar number is not provided instantaneously. The Aadhaar number is generated after some days of data collection. Processing of every enrollment requires matching ten fingerprints, both irises, and demographics with every existing record in the database. Currently, UIDAI is processing one million Aadhaar numbers a day. With the Aadhaar database at 600 million, processing 1 million enrollments every day roughly translates to about 600 trillion matches every day. 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 The number game Do you know how many years do one trillion seconds make? More than 31,000 years. Can you imagine the height of a tower that would be created by stacking one trillion pennies on top of each other? It will be more than 8,70,000 miles. One trillion ants will weigh more than 3000 tons. Six hundred trillion is a one followed by fourteen zeros. Besides storing such humongous amount of data, processing 600 trillion biometric matches in a day is beyond anyone’s wildest dreams. On the other hand, imagine if a person wants to open a bank account. He approaches a bank employee. This employee wants to check if this person is who he is claiming to be before opening his bank account. This authenticity check can’t run forever; then no customer will be willing to open an account with that bank. Authentication is expected to be performed within quick seconds, even when the authentication volume is a few 100 million requests every day. Authentication is synchronous and needs to happen very fast. 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 What’s the Difference between Data Science, Machine Learning and Big Data? Now let us see how the architectural principles established with UIDAI help in achieving the tasks of enrollment and authentication efficiently and effortlessly. Architectural Principles Scale-Up Up until the 90s Information Technology systems used to be monolithic, involving both technology and vendor lock-in. Once investment was made, it was challenging to break away from a particular vendor and technology. Advantage can’t be taken of the advancement in technology or drop in hardware and other costs. The only option was to ‘Scale-Up’ with the same vendor and technology. Scale-Out From the 90s to mid-2000s, the software with horizontal scaling capability at the application server layer came into existence. Even though it was possible to scale horizontally, it was tied up to a particular database vendor or application vendor. Here, there was no technology, but vendor lock-in. Here typically the computing environment, i.e. the hardware and OS used was similar across all application server nodes. A Love Story Begins with Open Scale-Out Open Scale-Out This phase started from mid-2000 onwards. Here the system architecture is vendor and technology neutral. There is no lock-in with any technology or vendor. Infinite scope for scaling and interoperability exists. UIDAI achieved open scale-out with the help of cheap commodity hardware. Commodity Hardware Commodity hardware is nothing but that which is affordable and accessible. It has nothing special in it which is typically used by enterprise systems. The entire UIDAI hardware infrastructure is composed of cheap Linux based personal computers and blade servers. The advantage of commodity hardware is that the cost and the initial investment are meager. The architecture is scalable when the requirement exists. Equipment can be purchased from any vendor and plugged in for scaling the architecture. The advantage of a price drop in the future can also be used while scaling the infrastructure. The open source technology, which is used to cluster commodity hardware is known as Hadoop. Distributed Computing & Open Source Imagine how it would be if a monolithic structure did all the processing work required for generating an Aadhaar card. How significant would that structure be? How many processing cores are needed for 600 trillion matches a day? Is it possible to expand that structure if the number of matches required increases from 600 to 1200 trillion? How costly would that be? For all these reasons, Aadhaar was implemented in a distributed commodity hardware. It is distributed not monolithic. The processing happens on many nodes at once, which reduces the execution times by many times. Distributed computing reduces the computation time, many times, which would take days in a traditional monolithic structure. The file system used in conventional sequential computing would not work in case of distributed computing. 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? A distributed platform requires a specially designed file system. Hadoop distributed file system (HDFS) is one such type of distributed file system. Special software is also needed to spread the workload between different nodes. On completion of processing at various nodes, this software should also aggregate the results. MapReduce is one such open source software which distributes and finally aggregates the processed results. Hive is a tool used to query the database distributed on the commodity hardware. Hive is very similar to SQL. What Skill Development Really Means and Why It’s Important for Success All these open source technologies like Hadoop, HDFS, MapReduce and Hive etc. come under the purview of Big data technologies. It is because of these technologies the processing time of computation, which would otherwise take days, can be reduced to mere minutes and at a very cheap cost. UIDAI entirely leveraged these technologies. It was implemented in a completely open scaleout fashion without any dependence on vendor or technology. Kudos Team UIDAI! Petabytes of data related to the identity of the citizens of a country, with a population more than one billion, is processed using open source technologies in a distributed fashion on commodity hardware. This is an astonishing feat of engineering which was successfully achieved by UIDAI. Team UIDAI deserves a thunderous applause for attaining this impossible feat. The government should now think of creative ways to leverage this data in avoiding leaks that happen in its various direct subsidy programs. It should bring more transparency to financial transactions, prevent tax evasion, provide banking facilities to the poor, and other such crucial tasks. Then, we can achieve the status of a real ‘welfare nation’. Wrapping up 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.

Do you know the skills and steps required to successfully transition to a Big Data career? If you’re someone who doesn’t belong to the Big Data Industry yet but has a background which may have links to it – you may be thinking about a lucrative and long-term Big Data career. If you’re aspiring to be a Big Data Engineer or a Team Lead/Tech Lead or even a Project Manager/Architect, there are some key technical skills required by employers in the Big Data Ecosystem. These skills vary for different Big Data Roles. In this article, we will discuss the technical skills required by employers for different Big Data profiles. We’ll also discuss organisational expectations from different hierarchical levels and steps to make a successful Big Data career transition. Essential Skills Here are the essential skills needed for making a successful Big Data career transition: Distributed Computing Big Data Environments You should have hands-on skills in at least one of the many Hadoop Distributions (viz. Hortonworks, Cloudera, MapR, IBM Infosphere BigInsights). At this point in time, Cloudera distribution is the most deployed distribution. Cloud Data Warehouses Since there is an increased affinity towards moving from on-premise data warehousing solutions to cloud-based data warehousing solutions, you should have skills in technologies like Amazon Redshift or Snowflake. Redshift is a fully managed cloud-based petabyte-scale data warehousing solution. NoSQL & NewSQL You should have skills in some of the new emerging NoSQL technologies. For e.g. MongoDB (which is a document database) or Couchbase (which is a key-value store). Others like Cassandra and HBase are also popular. On the cloud, Amazon has specific databases like DynamoDB and SimpleDB (both key-value pair stores). Data Integration & Visualisation As you work on large-scale analytics projects, you will be ingesting data from multiple sources. Keeping this in mind, you should have knowledge of Big Data compliant integration technologies like Flume, Sqoop, Storm Kafka etc. Data Integration products like Informatica and Talend have also upgraded their capabilities to Big Data processing. In the world of visualisation, Tableau and QlikView are popular. They also integrate with other BI (business intelligence) reporting data stores. Business Intelligence (BI) Hands-on knowledge of Business Intelligence technologies is also helpful. There are several technologies available in BI. For e.g. IBM, Oracle and SAP have acquired BI suites. Microsoft’s BI stack is largely organically developed. Others like Microstrategy and SAS are also independent BI providers. Big Data Testing Big Data Testing is fundamentally different from traditional ETL and application testing because of the volume of data involved. The differences in test scenarios occur due to the velocity and variety of data. Also, in certain cases, execution of test cases requires scripting and programming skills (Pig scripts, Hive query language etc.). Organisational Expectations and Hierarchical Responsibilities An organisation has different expectations from different levels of the workforce: Young Professionals (less than 5 years of overall experience) People in this age group mostly work as Big Data Engineers. As a Big Data Engineer, you are expected to be conversant with the above-mentioned technologies in the form of hands-on skills. As engineers, you would be responsible for building, testing and deploying the Big Data solutions. 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 Mid-Career Professionals (5 to 10 years overall experience)  People in this age group work as a team or tech leads. As a leader too, you are expected to be conversant in the above-mentioned technologies but will also be responsible for taking design decisions, conducting regular checkpoint reviews of the deliverables and providing overall technical guidance to the developers. Senior Professionals (overall experience of more than 10 years) Enterprise Architects: Enterprise architects are expected to be familiar with the above-mentioned technologies along with having a holistic view of the Big Data Landscape. As an architect, you are expected to be trusted partners of the clients, advising them on the right architecture, transformation strategy and roadmap, tool selection and vendor evaluation. Project Managers: For a PM, managing a Big Data project team requires cross-functional team management skills – data warehousing teams, Business Intelligence teams, statisticians, domain experts and data teams. Knowledge management is another key skill. It is important to understand and plug knowledge gaps in the team. Further, a Big Data PM is expected to understand Agile methodologies to deliver the projects. What’s the Difference between Data Science, Machine Learning and Big Data? 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 Transitioning to Big Data The best way to make a Big Data career transition is by acquiring the relevant skills and then applying them in case studies/projects that simulate real-life scenarios. These could be part of a training program/education program, or through shadowing in-flight projects (or Proof of Concepts – PoCs) in existing organisations, wherever possible. The following is a breakdown of the kind of activities practitioners can do in these case studies, according to the experience levels. Young Professional (less than 5 years of overall experience) You should be looking to acquire the skills through training programs/PoCs and then apply them to projects that simulate real-life scenarios. Mid Career Professional (5 to 10 years overall experience) You should drive technology solution discussions, coming up with designs and conducting reviews of work products and guiding teams during the case studies. 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   Senior Professionals (overall experience of more than 10 years) You should be the one who kick-starts the execution of the case studies, acquiring a clear understanding of functional requirements, developing the solution strategy to meet project requirements within stipulated timelines and developing the project charter (PM roles) and overall technology solution (Architect roles). This takes us to the question: 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 What should you look for in a good Big Data Program or Course? The course should provide the right enablers for the participants to complete a Big Data career transition into these roles. The following are the 3 key expectations you should have of any course: Technical skills: The course should impart the above-mentioned skills through a suitably designed curriculum. Cloud platform: You should get access to a cloud platform with the relevant software and experiment with it. Case studies/Projects: The course should have a simulation of real-life scenarios as explained above, where participants in the various categories can play out the roles as explained above. 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? 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.

The consumer market today is becoming more and more competitive and companies are struggling to offer something unique to their consumers. To be able to do that, companies need to understand the consumers better. The primary way to get meaningful consumer insights is to analyse the existing data collected from users. These insights can then be used not only to continue selling the products but provide customised events and service, which are available at a premium. This trend is fairly common in new age industries such as e-commerce, even traditional, centuries-old industries greatly benefit from big data and analytics applications. For example, by installing sensors and subsequently analysing them, a railway operator can analyse their fixed and rolling assets. Big data analytics can identify when to carry out preventive maintenance on assets such as bridges and railway lines, increasing economic life and reducing downtime. Hence, data is not just benefitting new-age industries, but the traditional industries as well. Here are some of the most commonly used big data applications around you, across industries: Retail Companies collect data of individual customers, the type of purchases they’re making and more importantly where they’re making the purchases. Based on this information, companies are able to segment customers according to their buying behavior. They then make predictions on what they will be buying in the future. This data is also used to cross-sell or upsell items, with the help of attractive offers on these new items. Location Another big use of data in analytics is to map areas or locations, as well known by everyone who uses Uber or Ola or Google Maps. Even food delivery apps and other apps that deliver goods to your doorsteps know where you live/work, etc. A huge amount of data gets captured every time you order and it includes all location characteristics in it. This information is also mined from a public policy perspective to look for traffic jams and also for taking decisions like setting up public transportation facilities such as metro stations. 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 Energy The advent of big data has had a huge impact on the energy sector. Big data involves a large number of sensors and data collection methodologies which have allowed for the setting up of large systems for preventive maintenance. It enables better forecasting of demand. For example, ten years ago, there were no smart meters. Now, the power utility sector has very good information on how their consumers are consuming their power, the time, and the load that is consumed. This is actually helping them to make their investment decisions much faster. These industries are becoming more efficient both in terms of cost and in operation. Telecom Every operator is searching for new ways to increase profits during a time of stagnant and competitive growth in the industry. Here is where telecom companies are advancing rapidly in terms of being able to capture data and use it wisely for a variety of uses. Companies around the world are using big data to gain market share with targeted promotions, combating fraud, improving customer experiences and designing newer product offerings. 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 Automotive This sector is actually now trying to become more connected. Self-driving cars that we all already know about is one of the biggest buzzwords. Underneath it, to make this possible, there is a huge amount of technology that vehicles are collecting, gathering and using in conjunction to come up with these advancements. Increased government encouragement of electric vehicles requires location analytics to establish charging stations. 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 What lies ahead? The only thing that is going to hold back the Big Data industry is the number of people who are skilled in it. The big data applications are actually limitless. There is a huge demand for skilled people at all levels from project managers to raw beginners. As a practitioner who’s been in this industry for some time, I can tell you that there is a huge demand. Companies are facing a talent problem at all levels and the solutions also have to come from different sources, such as increased access to education, training initiatives by companies, awareness spreading by the government. The 11-month BITS Pilani and UpGrad program for working professionals is exactly the type of program that we need to help people who are ambitious, keen on furthering their careers and following their passions. I think a course like this is very useful because you have a large number of people who come from the industry and are excited to teach. Students will benefit a lot from learning hands-on and through practitioners directly. I am fairly certain that it will involve a lot of problem-solving and casework type methodology. So, I think people are going to have fun while they’re at it. I think that’s especially important when you are doing something on your weeknights and weekends. 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? Views shared in this blog are the author’s personal views and they do not reflect the official stance of The Boston Consulting Group (BCG) or any of the author’s clients. Conclusion 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.

Cancer is not one disease. It is many diseases. Let us understand the cause of cancer by a simple example. If you take a photocopy of a document, due to some issues, other dots or smears appear on it even though they are not present in the original copy. In the same way, in gene replication processes, errors occur inadvertently. Most of the time the genes with errors will not be able to sustain and will ultimately perish. In some rare cases, the mutated gene with mistakes will survive and get further replicated uncontrollably. Uncontrollable replication of mutated genes is the primary cause of cancer. This mutation can happen in any of the twenty thousand genes in our body. Variation in any one or a combination of genes makes cancer a severe disease to conquer. To eradicate cancer, we need methods to destroy the rogue cells without harming the functional cells of the body; which makes it doubly hard to defeat. Cancer and its complexity Cancer is a disease with a long tail distribution. Long tail distribution means there are various reasons for this condition to occur and there is no single solution for eradicating it. There are diseases which affect a large percentage of the population but have a sole cause of occurrence. For example, let us consider Cholera. Eating food or drinking water contaminated by the bacterium Vibrio Cholerae is the cause of cholera. Cholera can occur only because of Vibrio Cholerae, and there is no another reason. Once we find out the only cause of a disease, then it is relatively easy to conquer it. What if a condition occurs because of multiple reasons? A mutation can occur in any of the twenty thousand genes in our body. Not only that, but we also need to consider their combinations. Cancer may not just happen because of a random mutation in a gene but also because of a combination of gene mutations. The number of causes for cancer becomes exponential, and there is no single mechanism to cure it. For example, a mutation of any of these genes ALK, BRAF, DDR2, EGFR, ERBB2, KRAS, MAP2K1, NRAS, PIK3CA, PTEN, RET, and RIT1 can cause lung cancer. There are many ways for cancer to occur and that’s why it is a disease with long tail distribution. In our arsenal for waging this war on cancer and conquering it, big data and machine learning are critical tools. How can big data help in fighting this war? What does machine learning have to do with cancer? How are they going to help in fighting a disease with many causes, a condition with a long tail distribution? Firstly, how and where is this big data generated? Let us find answers to these questions. Gene Sequencing and explosion in data Gene sequencing is one area which is producing humongous amounts of data. Exactly how much data? According to the Washington Post, the human data generated through gene sequencing (approximately 2.5 lakh sequences) takes up about a fourth of the size of YouTube’s yearly data production. If all this data were combined with all the extra information that comes with sequencing genomes and recorded on 4GB DVDs, it would be a stack about half a mile high. 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 The methods for gene sequencing have improved over the years, and the cost for the same has plummeted exponentially. In the year 2008, the cost of gene sequencing was 10 million dollars. As of today, it is only a 1000 dollars. In the future, it is expected to reduce further. It is estimated that one billion people will have their genes sequenced by 2025. So, within the next decade, the genomics data generated will be somewhere between 2 – 40 exabytes in a year. An exabyte is ten followed by 17 zeros. Before coming to how data will help in curing cancer, let us take one concrete example and see how data can help in conquering a disease. Data and its analysis helped in finding out the cause of one infectious disease and fight it, not now but in nineteenth-century itself! Yes, in the nineteenth century! The name of that disease is Cholera. Clustering in the Nineteenth Century – the Cholera breakthrough John Snow was an anesthesiologist and cholera broke out in September 1854 near Snow’s house. To know the reason for cholera, Snow decided to note the spatial dimensions of the patients on the city map. He marked the location of the home address of patients on London’s city map. With this exercise, John Snow understood that people suffering from cholera were clustered around some specific water wells. He firmly believed that a contaminated pump was responsible for the epidemic and against the will of the local authorities replaced the pump. This replacement drastically reduced the spread of cholera. Snow subsequently published a map of the outbreak to support his theory, showing the locations of the 13 public wells in the area, and the 578 cholera deaths mapped by home address. This map ultimately led to the understanding that cholera was an infectious disease and quickly spread through the medium of water. John Snow’s experiment is the earliest example of applying the clustering algorithm to know the cause of illness and help eradicate it. In the nineteenth century, John Snow could apply clustering algorithm on a London city map with a pencil. With cancer as the target disease, this level of analysis is not possible with the same ease as John Snow’s Analysis. We need sophisticated tools and technologies to mine this data. That is where we leverage the capabilities of modern technologies like Machine Learning and Big Data. 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 and Machine learning – tools to fight cancer Vast amounts of data along with machine learning algorithms will help us in our fight with cancer in many ways. It can help us with diagnosis, treatment, and prognosis. Mainly, it will help customise the therapy according to the patient, which is not possible otherwise. It will also help deal with the long tail of the distribution. Given the enormous amounts of Electronic Medical Records (EMR), data generated and recorded by various hospitals; it is possible to use ‘labelled’ data in diagnosing cancer. Techniques like Natural Language Programming (NLP) are utilised for making sense of doctor’s prescriptions and Deep Learning Neural Networks are deployed to analyse CT and MRI scans. The different types of machine learning algorithms search the EMR databases and find hidden patterns. These hidden patterns will help in diagnosing cancers. A college student was able to design an Artificial Neural Network from the comfort of her home and developed a model that can diagnose breast cancer with a high degree of accuracy. 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 Diagnosis with Big Data and Machine Learning Brittanny Wenger was 16 years old when her older cousin was diagnosed with breast cancer. This inspired her to make the process better by improving the diagnostics. Fine Needle Aspiration (FNA) was a less invasive method of biopsy and the quickest method of diagnosis. The doctors were reluctant to use FNA because the results are not reliable. Brittanny thought of using her programming skills to do something about it. She decided to improve the reliability of FNA which would enable the women to choose less invasive and comfortable diagnostic methods. Brittanny found public domain data from the University of Wisconsin that included Fine Needle Aspiration. She coded an Artificial Neural Network (ANN) which is inspired by the design of human brain architecture. She used cloud technologies to process the data and train the ANN to find the similarities. After many attempts and errors finally, her network was able to detect breast cancer from an FNA test data with 99.1% sensitivity to malignancy. This method is applicable for diagnosing other cancers as well. The accuracy of diagnosis is dependent upon the amount and quality of the data available. The more the data available, the more the algorithms will be able to query the database, find similarities and come out with valuable models. Treatment with Big Data and Machine Learning Big data and Machine learning will be helpful not only for diagnosis but treatment as well. John and Kathy were married for three decades. At the age of 49, Kathy was diagnosed with stage III breast cancer. John, CIO of a Boston hospital helped plan her treatment with the help of big data tools that he designed and brought into existence. In 2008, five Harvard affiliated hospitals shared their databases and created a powerful search tool known as ‘Shared Health Research Information Network’ (SHRINE). By the time of Kathy’s diagnosis, her doctors could sift through a database of 6.1 million records to find insightful information. Doctors queried ‘SHRINE’ with questions like “50-year-old Asian women, diagnosed with stage III breast cancer and their treatments”. Armed with this information doctors were able to treat her with chemotherapy drugs by targeting the estrogen-sensitive tumour cells by avoiding surgery. By the time Kathy completed her chemotherapy regimen the radiologists could no longer find any tumour cells. This is one example of how big data tools can help in customising the treatment plan according to the requirement of each. As cancer is a long tail distribution a ‘one size fits all’ philosophy will not work. For customising treatments depending on the patient’s history, their gene sequence, results of diagnostic tests, a mutation found in their genes or a combination of their genes and environment, big data and machine learning tools are indispensable. 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   Drug Discovery with Big Data and Machine Learning Big data and Machine learning will not only help in diagnosis and treatment but also will revolutionise drug discovery. Researchers can use open data and computational resources to discover new uses for the drugs which are already approved by agencies like FDA for other purposes. For example, scientists at University of California at San Francisco found by number crunching that a drug called ‘pyrvinium pamoate’ which is used to treat pinworms – could shrink hepatocellular carcinoma, a type of liver cancer, in mice. This disease which is associated with the liver is the second highest contributor to cancer deaths in the world. Not only is big data used for discovering new uses for old drugs but can also be used for detecting new drugs. By crunching data related to different drugs, chemicals, and their properties, symptoms of various diseases, the chemical composition of the drugs used for those conditions and side effects of these medications collected from different media; new drugs can be devised for various types of cancer. This will significantly reduce the time taken to come up with new medicines without wasting millions of dollars in the process. Using big data and machine learning will no doubt improve the process of diagnosis, treatment and drug discovery in treating cancer, but it is not without challenges. There are many stumbling blocks and problems on the road ahead. If these blocks are not removed, and these challenges are not faced, then our enemy will get the upper hand and will defeat us in the future battle. 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? Challenges in using Big Data and Machine Learning to fight Cancer Digitisation Except for a few large and technically advanced hospitals, most of them are yet to be digitised. They are still following the old methods of capturing and recording data in massive stacks of files. Due to lack of technical expertise, affordability, economies of scale and various other reasons, digitisation has not taken place. Provision of open source EMR software, teaching how helpful these digital records could be in treating the patients and how profitable it is to the hospitals are some steps in the right direction. Data locked in enterprise warehouses As of today, only a few hospitals can digitally capture patient records. This apparatus too is locked away in enterprise warehouses and inaccessible to the world at large. Hospitals are reluctant to share their databases with other hospitals. Even if they are willing, they are plagued by the different database schemas and architectures. Critical thinking is required on this front about how hospitals can share their databases among themselves for their mutual benefit without being suspicious of each other. A consensus needs to be reached about the schema in which this data should be shared as well, for the benefit of all hospitals. This patient data should be democratised and utilised for the betterment of the future of mankind.   Patient data should not be allowed to be employed for the growth of a single organisation. Utmost care should be taken to anonymise the individual to whom the data belongs. If a person’s lipstick preference is leaked, then there is not much harm. If a person’s medical history is leaked, then it will have a significant impact on his life and prospects. The government should take positive steps in this direction and should help create a big data infrastructure for storing medical records of patients from all hospitals. It should make it compulsory for all hospitals to share their database within this shared infrastructure. Access to this database should be made free for patient treatment and research. Improvement in efficiency of Machine Learning Algorithms Machine learning is not a magic pill for cancer diagnosis and treatments. It is a tool that if used well can help in our journey to conquer cancer. Machine learning is still in a nascent stage and has its disadvantages. For example, the data on which these algorithms are trained needs to be very close to the data on which they are utilised for producing results. If there is a huge difference in them, then the algorithm will not be able to provide meaningful results which can be employed. There are many machine learning algorithms which exist with their own peculiar assumptions, advantages, and disadvantages. If we can find a way to combine all these different algorithms for achieving the results required by us, i.e. curing cancer, needless to say, we would have found a hugely beneficial outcome. The famous machine learning scientist Pedro Domingos calls it “The Master Algorithm”, who also wrote a popular science book of the same name. According to Pedro, there are five different schools of thought in machine learning. The symbolist, connectionist, Bayesian, evolutionaries and analogisers. It is difficult to go into all these different types of machine learning systems in this article. I will cover all the five types of machine learning systems in one of my future blogs. For now, we need to understand that all these different methods have advantages and disadvantages of their own. If we can combine them, then we can derive highly impactful insights from our data. This will be immensely useful not only for all kinds of predictions and forecasts but also for our fight against a vengeful enemy – cancer. To summarise, cancer is a formidable enemy which keeps changing its form frequently. We do possess new weapons in our arsenal now in the form of big data and machine learning, however, to face it competently. But to demolish it entirely we need a more powerful weapon than what we presently possess. The name of that weapon is ‘The Master Algorithm’. We also need to make some changes in the strategies and methods with which we are fighting this enemy. These changes are creating a big data infrastructure, making it compulsory for hospitals to share anonymised patient records, maintaining the security of the database and allowing free access to the database for patient treatment and research to cure cancer. Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Wrapping up If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Engineering degrees online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.