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Kick-start Your Career in Big Data and Hadoop

    We are sure, like everybody else you have heard about how Big Data is taking the world by storm and how a career in Hadoop can really take your future places. But we are also sure, like everybody else you have scores of questions but just don’t know who to ask or get guidance from.

    A recent study from the global consulting powerhouse Mckinsey has revealed that for the first time the exchange of cross border data is contributing more to the global GDP than the global exchange of goods (international trade to be precise). In short, data is the new trade that the world is enamored to. Do you know that the cross-border data exchange via the undersea internet cables has grown 45 times since 2005? Now this is further expected to grow another 9 times within the next five years. All that is too much of data on the grandest of the scales to even fathom, right?

    So the natural question that you might ask is where does all this data emanate from? Well, the biggest contributor is internet videos, our penchant for watching them on YouTube or Facebook, then there are our online conversations and interactions on social networking sites, our ecommerce shopping data, financial and credit card information, all the website content, blogs, infographics, statistics and so on and so forth. But wait, we now have a new contributor to this league and this comes from the various machines which are connected to the internet in the grand scheme of things called the Internet of Things. In future IoT will be the biggest contributor of data exchange going from one end of the globe to the other at the speed of light over our internet networks. So that is all about Big Data for you in a nutshell.

    So now your doubt about what comprises Big Data and how big it really is has been cleared once and for all, we can move on. So this should naturally convince you to take up a career in Big Data and Hadoop. The next question that you might have – what is Hadoop and why do I need to learn it? Hadoop is a software framework for processing of large amounts of data on a level that is just too overwhelming for regular database processing tools or software, for that matter. Scalability is one of the hallmarks of Hadoop as it can be replicated from a single machine to thousands, as and when the need arises. Hadoop is agile, resilient, versatile among other features making it the most important Big Data processing framework available today.

    Prerequisites for a career in Hadoop

    Let us start by saying that the first and foremost thing is a love for working with data, an inquisitive mind, a mindset that is ready to work out of the box and a creative thought process. This is not to confuse you in any manner but all we want to say is that the technical knowhow and the programming skills can all be acquired with the right frame of mind in a very short duration of time.

    So if you are really keen on having a career in Hadoop then we suggest you start by learning the nuances of any programming language be it Java, Perl, Python, Ruby or even C. Once you have a basic framework of how programming languages work and how to write a highly effective algorithm then you are good to go to begin learning Hadoop.

    Career prospects upon completion of Hadoop Certification

    Let us reiterate, since the amount of data is only going to increase in the future and the field of Big Data hasn’t yet been saturated, you can expect a stellar career growth opportunity with the right skill sets and domain level expertise of Hadoop. In India, with just a few years of experience professionals in Big Data can command a salary of upwards of Rs. 10 Lakhs per annum. While there are many different roles and job opportunities in the Big Data domain, you can choose one that suits you the best among the ones mentioned herein – Hadoop Architect, Hadoop Developer, Data Scientist, Data Visualizer, Research Analyst, Data Engineer, Data Analyst, and Code Tester.

    Industries that are currently hiring Hadoop professionals

    Today we have reached a phase where working with huge amounts of data is imperative for organizations regardless of their industry vertical and customer segmentation. So expect a whole host of industry verticals vying for your attention in their pursuit of hiring the best talent in Big Data and Hadoop. Some of the popular business sectors currently hiring are banking, insurance, ecommerce, hospitality, manufacturing, marketing, advertising, social media, healthcare, transportation, and the list can be almost endless. There will be an overwhelming need for at least 1.5 million Big Data professionals and analysts by 2018 in the United States alone. Know that most of these could be filled by talented Indian professionals since India is the biggest talent pool in the IT sector currently in the world!

    How all that Big Data is being used by business enterprises?

    Today our world is firmly in a knowledge economy. It is no longer about how much capital or manpower your business organization has. But it inevitably boils down to how much data, information and knowledge that you have in the grand scheme of things. Data is the new arms race and the global multinational enterprises are the new superpowers.

    Let’s consider a few examples to elucidate this:

    Some of the largest technology companies of the world viz. Google, Apple, Amazon, EBay and Facebook run purely on the power of Big Data that they are accessing and making sense of in order to gain a definitive advantage over their rivals. It is vital to emphasize that more data is not just something about that is a little bit extra – more distinctively signifies new, more signifies better and more even signifies something radically different. Did you know that using IBM Watson cognitive computing technology the doctors in the United States were able to detect new symptoms of cancer the ones which they never knew existed in the first place. All this thanks to Big Data!

    Among other things, Big Data helps organizations to understand their customers better, detect patterns, segregate demographics, anticipate sales, and better pitch the products and services to the customers in a more personalized and user-friendly manner. It assists enterprises to predict the future, understand growth opportunities, look for newer markets and better ways to service the customers among a million other things that only Big Data can afford these forward-thinking enterprises.

    Finally, remember that only 0.5% of all the data that is available today has ever been analyzed or utilized. So the whole sea of information lies right in front of us and that needs the unflinching support of all individuals in their professional capacities like you to steer the global economy to the next orbit of growth and prosperity. So are you game to take up a career in Big Data and Hadoop now. One thing we can assure you is that with Big Data and Hadoop your career will never be the same again.

    Most Valuable Data Science Skills Of 2020

     “Information is the oil of the 21st century, and analytics is the combustion engine.”– Peter Sondergaard, SVP, Gartner Research.

    So you have heard how data science is one of the best jobs of the 21st century. Since it is a relatively young domain the scope and opportunities in this field are aplenty for people with the right set of skills and qualifications.

    2019 could be the defining year in the Data Science domain.

    Today regardless of the size of a business or industry type there is a need for quality professionals who can decipher all that unstructured and strategize business goals. So let’s delve deeper into the skills that are needed to pursue a career in the Big Data sphere.

    It’s a no-brainer that technical skills are a must for any data scientist. Having the right education matters a lot. It could be an Engineering degree, a Master’s degree or even a PhD in a field of your choice. Having an analytical bent of mind goes a long way in securing your future in this arena.

    Some of The Popular Fields of Science That Are Much Sought-After

    • Statistics and Mathematics
    • Computer Science
    • Analytical Reasoning

    When it comes to analytical skills, most companies are looking for people with skill either in SAS or R programming. Though for data science, R is the most preferred analytical tool.

    Technical Expertise and Coding Skills

    Knowledge of Hadoop: since Hadoop is the most popular Big Data framework it is expected that you have a good understanding of it. This includes MapReduce processing and working with Hadoop Distributed File System (HDFS).

    Programming Skills:  there are various programming languages most common of which are Python, Java, C, or Perl. Having a good grasp of any of these coding languages will be very useful.

    SQL database: SQL is the most common way of getting information from a database and updating it. Candidates need to know how to query in SQL since it is the most preferred language for RDBMS.

    NoSQL: the range of today’s data collected is so wide and diverse that SQL alone cannot provide all the solutions. This is where NoSQL takes over in order to make sense of databases that are not in tabular form and are thus more complex.

    Non-Technical Expertise

    Intellectual Quest:  though the technical skills are vital for a successful career in the data science domain, it is by far not the only requirement. The candidates should have a strong thirst for knowledge and initiative to use their intelligence to parse a problem. It is the skill to not only understand “what is” but rather “what can be” when it comes to Big Data applications.

    Strong business acuity:  all data science personnel will be working in a business environment and hence a clear understanding of the business domain is a must-have skill. Knowledge of what real world problems your organization is trying to solve is expected plus a knack to deploy data in newer ways so that your organization can benefit in hitherto unheard ways.

    Excellent communication: a large part of the job of a data scientist involves communicating with different departments in order to get the work done. Sometimes he has to be the liaison between the technical and non-technical staff. Thus a complete knowledge of the industry is a must. Apart from that he has to have good management and people skills in order to take all stakeholders into confidence.

    Skill Set Advancement

    Online courses: a lot of online training courses and tutorials are available in order to help freshers and seasoned professionals alike to make it big in the data science domain.

    Professional Certification: companies like IBM, Cisco are at the forefront of ensuring that the right candidates get the right jobs. Hence they are provided industry recognized certifications upon completion of certain courses and training for the worthy candidates.

    Reputable Hackathons: if you are living in a city that has a vibrant IT ecosystem (like San Francisco in the USA and Bangalore in India) then chances are that you will have regular Hackathons wherein the programmers and other technical professionals meet and work on short intense projects that have huge real world significance.

    Big Data Job Responsibilities and Skills

    Hadoop is the new data warehouse. It is the new source of data within the enterprise. There is a premium on people who know enough about the guts of Hadoop to help companies take advantage of it. – James Koibelus, Analyst at Forrester Research

    Big Data and Big Data jobs are everywhere. Let’s leave the clichés behind and cut to the chase: a Hadoop professional can earn an average salary of $112,000 per year and, in San Francisco, it can go up to $160,000. Now that we have your undivided attention, let us delve into what exactly is meant by being a Hadoop professional and what the roles and responsibilities of a Hadoop professional are.

    General Skills Expected from Hadoop Professionals

    • Ability to work with huge volumes of data so as to derive Business Intelligence
    • Knowledge to analyze data, uncover information, derive insights, and propose data-driven strategies
    • Knowledge of OOP languages like Java, C++, and Python
    • Understanding of database theories, structures, categories, properties, and best practices
    • Knowledge of installing, configuring, maintaining, and securing Hadoop
    • Analytical mind and ability to learn-unlearn-relearn concepts

    What Are the Various Job Roles Under the Hadoop Domain?

    • Hadoop Developer
    • Hadoop Architect
    • Hadoop Administrator
    • Hadoop Tester
    • Data Scientist

    The US alone faces a shortage of 1.4–1.9 million Big Data Analysts!

    Hadoop Developer Roles and Responsibilities

    The primary job of a Hadoop Developer involves coding. They are basically software programmers, working in the Big Data Hadoop domain. They are adept at coming up with the design concepts that are used for creating extensive software applications. They are masters of computer procedural languages.

    A professional Hadoop Developer can expect an average salary of US$100,000 per annum!

    Below are duties you can expect as part of your Hadoop Developer work routine:

    • Have knowledge of the agile methodology for delivering software solutions
    • Design, develop, document, and architect Hadoop applications
    • Manage and monitor Hadoop log files
    • Develop MapReduce coding that works seamlessly on Hadoop clusters
    • Have working knowledge of SQL, NoSQL, data warehousing, and DBA
    • Be an expert in newer concepts like Apache Spark and Scala programming
    • Acquire complete knowledge of the Hadoop ecosystem and Hadoop Common
    • Seamlessly convert hard-to-grasp technical requirements into outstanding designs
    • Design web services for swift data tracking and query data at high speeds
    • Test software prototypes, propose standards, and smoothly transfer them to operations

    Most companies estimate that they’re analyzing a mere 12 percent of the data they have!

    Hadoop Architect Roles and Responsibilities

    A Hadoop Architect, as the name suggests, is someone who is entrusted with the tremendous responsibility of dictating where the organization will go in terms of Big Data Hadoop deployment. He is involved in planning, designing, and strategizing the roadmap and deciding how the organization moves forward.

    Below are duties you can expect as part of your Hadoop Architect work routine:

    • Have hands-on experience in working with Hadoop distribution platforms like Hortonworks, Cloudera, MapR, and others
    • Take end-to-end responsibility of the Hadoop life cycle in the organization
    • Be the bridge between Data Scientists, Engineers, and the organizational needs
    • Do in-depth requirement analyses and exclusively choose the work platform
    • Acquire full knowledge of Hadoop architecture and HDFS
    • Have working knowledge of MapReduce, HBase, Pig, Java, and Hive
    • Ensure to choose a Hadoop solution that would be deployed without any hindrance

    75 percent of companies are investing or planning to invest in Big Data already – Gartner

    Hadoop Administrator Roles and Responsibilities

    Hadoop Administrator is also a very prominent role as he/she is responsible for ensuring that there is no roadblock to the smooth functioning of the Hadoop framework. The roles and responsibilities of this job profile resemble that of a System Administrator. A complete knowledge of the hardware ecosystem and Hadoop architecture is critical.

    A certified Hadoop Administrator can expect an average salary of US$123,000 per year!

    Below are duties you can expect as part of your Hadoop Administrator work routine:

    • Manage and maintain Hadoop clusters for uninterrupted job
    • Check, back-up, and monitor the entire system, routinely
    • Ensure that the connectivity and network are always up and running
    • Plan for capacity upgrading or downsizing as and when the need arises
    • Manage HDFS and ensure that it is working optimally at all times
    • Secure the Hadoop cluster in a foolproof manner
    • Regulate the administration rights depending on the job profile of users
    • Add new users over time and discard redundant users smoothly
    • Have full knowledge of HBase for efficient Hadoop administration
    • Be proficient in Linux scripting and also in Hive, Oozie, and HCatalog

    For a Fortune 1000 company, a 10 percent increase in data accessibility can result in US$65 million additional income!

    Hadoop Tester Roles and Responsibilities

    The job of a Hadoop Tester has become extremely critical since Hadoop networks are getting bigger and more complex with each passing day. This poses some new problems when it comes to viability and security and ensuring that everything works smoothly without any bugs or issues. A Hadoop Tester is primarily responsible for troubleshooting Hadoop applications and rectifying any problem that he/she discovers at the earliest before it becomes seriously threatening.

    An expert Hadoop Testing Professional can earn a salary of up to US$132,000 per annum!

    Below are duties you can expect as part of your Hadoop Tester work routine:

    • Construct and deploy both positive and negative test cases
    • Discover, document, and report bugs and performance issues
    • Ensure that MaReduce jobs are running at peak performance
    • Check if the constituent Hadoop scripts like HiveQL and Pig Latin are robust
    • Have expert knowledge of Java to efficiently do the MapReduce testing
    • Understand MRUnit, and JUnit testing frameworks
    • Be fully proficient in Apache Pig and Hive
    • Be an expert to work with the Selenium Testing Automation tool
    • Be able to come up with contingency plans in case of breakdown

    Data Scientist Roles and Responsibilities

    Data Scientist is a much sought-after job role in the market today and there aren’t enough qualified professionals to take up this high-paying job that enterprises are ready to offer. What makes a Data Scientist such a hot commodity in the jobs market? Well, a part of the allure lies in the fact that a Data Scientist wears multiple hats over the course of a typical day at office. He is a scientist, an artist, and a magician!

    The average salary of a Data Scientist is US$123,000 per annum!

    So far, only less than 0.5 percent of all data is ever analyzed and used!

    Data Scientists are basically Data Analysts with wider responsibilities. Below are duties you can expect as part of your Data Scientist work routine:

    • Master different techniques of analyzing data, completely
    • Expect to solve real business issues backed by solid data
    • Tailor the data analytics ecosystem to suit the specific business needs
    • Have a strong grip of mathematics and statistics
    • Keep the big picture in mind at all times to know what needs to be done
    • Develop data mining architecture, data modeling standards, and more
    • Have an advanced knowledge of SQL, Hive, and Pig
    • Be an expert to work with R, SPSS, and SAS
    • Acquire the ability to corroborate actions with data and insights
    • Have creativity to do things that can make wonders for the business
    • Have top-notch communication skills to connect with everybody on-board in the organization

    Big Data Terminologies You Must Know

    We will discuss the terminology related to Big Data ecosystem. This will give you a complete understanding of Big Data and its terms.

    Over time, Hadoop has become the nucleus of the Big Data ecosystem, where many new technologies have emerged and have got integrated with Hadoop. So it’s important that, first, we understand and appreciate the nucleus of modern Big Data architecture.

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers, using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

    Components of the Hadoop Ecosystem

    Let’s begin by looking at some of the components of the Hadoop ecosystem:

    Hadoop Distributed File System (HDFS™):

    This is a distributed file system that provides high-throughput access to application data. Data in a Hadoop cluster is broken down into smaller pieces (called blocks) and distributed throughout the cluster. In this method, the map and reduce functions can be executed on smaller subsets of your larger data sets, and this provides the scalability needed for Big Data processing.


    MapReduce is a programming model specifically implemented for processing large data sets on Hadoop cluster. This is the core component of the Hadoop framework, and it is the only execution engine available for Hadoop 1.0.

    The MapReduce framework consists of two parts:

    1. A function called ‘Map’, which allows different points in the distributed cluster to distribute their work.

    2. A function called ‘Reduce’, which is designed to reduce the final form of the clusters’ results into one output.

    The main advantage of the MapReduce framework is its fault tolerance, where periodic reports from each node in the cluster are expected as soon as the work is completed.

    The MapReduce framework is inspired by the ‘Map’ and ‘Reduce’ functions used in functional programming. The computational processing occurs on data stored in a file system or within a database, which takes a set of input key values and produces a set of output key values.

    Each day, numerous MapReduce programs and MapReduce jobs are executed on Google’s clusters. Programs are automatically parallelized and executed on a large cluster of commodity machines.

    Map Reduce is used in distributed grep, distributed sort, Web link-graph reversal, Web access log stats, document clustering, Machine Learning and statistical machine translation.


    Pig is a data flow language that allows users to write complex MapReduce operations in simple scripting language. Then Pig then transforms those scripts into a MapReduce job.


    Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage. Hive provides a mechanism for querying the data using a SQL-like language called HiveQL. At the same time, this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL.


    Enterprises that use Hadoop often find it necessary to transfer some of their data from traditional relational database management systems (RDBMSs) to the Hadoop ecosystem.

    Sqoop, an integral part of Hadoop, can perform this transfer in an automated fashion. Moreover, the data imported into Hadoop can be transformed with MapReduce before exporting them back to the RDBMS. Sqoop can also generate Java classes for programmatically interacting with imported data.
    Sqoop uses a connector-based architecture that allows it to use plugins to connect with external databases.


    Flume is a service for streaming logs into Hadoop. Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming data into the Hadoop Distributed File System (HDFS).


    Storm is a distributed, real-time computation system for processing large volumes of high-velocity data. Storm is extremely fast and can process over a million records per second per node on a cluster of modest size. Enterprises harness this speed and combine it with other data-access applications in Hadoop to prevent undesirable events or to optimize positive outcomes.


    Apache Kafka supports a wide range of use cases such as a general-purpose messaging system for scenarios where high throughput, reliable delivery, and horizontal scalability are important. Apache Storm and Apache HBase both work very well in combination with Kafka.


    Oozie is a workflow scheduler system to manage Apache Hadoop jobs. The Oozie Workflow jobs are Directed Acyclical Graphs (DAGs) of actions, whereas the Oozie Coordinator jobs are recurrent Oozie Workflow jobs triggered by time (frequency) and data availability.

    Oozie is integrated with the rest of the Hadoop stack and supports several types of Hadoop jobs out of the box (such as Java map-reduce, Streaming map-reduce, Pig, Hive, Sqoop and Distcp) as well as system-specific jobs (such as Java programs and shell scripts). Oozie is a scalable, reliable and extensible system.


    Apache Spark is a fast, in-memory data processing engine for distributed computing clusters like Hadoop. It runs on top of existing Hadoop clusters and accesses the Hadoop data store (HDFS).

    Apache Solr:

    Apache Solr is a fast, open-source Java search server. Solr enables you to easily create search engines that search websites, databases, and files for Big Data.

    Apache Yarn:

    Apache Hadoop YARN (Yet Another Resource Negotiator) is a cluster management technology. YARN is one of the key features in the second-generation Hadoop 2 version of the Apache Software Foundation’s open-source distributed processing framework. Originally described by Apache as a redesigned resource manager, YARN is now characterized as a large-scale, distributed operating system for big data applications.


    Tez is an execution engine for Hadoop that allows jobs to meet the demands for fast response times and extreme throughput at petabyte scale. Tez represents computations as a dataflow graphs and can be used with Hadoop 2 YARN.

    Apache Drill:

    Apache Drill is an open-source, low-latency query engine for Hadoop that delivers secure, interactive SQL analytics at petabyte scale. With the ability to discover schemas on the go, Drill is a pioneer in delivering self-service data exploration capabilities on data stored in multiple formats in files or NoSQL databases. By adhering to ANSI SQL standards, Drill does not require a learning curve and integrates seamlessly with visualization tools.

    Apache Phoenix:

    Apache Phoenix takes your SQL query, compiles it into a series of HBase scans, and co-ordinates the running of those scans to produce a regular JDBC result set. Apache Phoenix enables OLTP and operational analytics in Hadoop for low-latency applications by combining the best of both worlds. Apache Phoenix is fully integrated with other Hadoop products such as Spark, Hive, Pig, Flume, and Map Reduce.

    Cloud Computing:

    Cloud Computing is a type of computing that relies on sharing computing resources rather than having local servers or personal devices to handle applications. Cloud Computing is comparable to grid computing, a type of computing where the unused processing cycles of all computers in a network are harnessed to solve problems that are too processor-intensive for any single machine.

    In Cloud Computing, the word cloud (also phrased as “the cloud”) is used as a metaphor for the Internet, hence the phrase cloud computing means “a type of Internet-based computing” in which different services such as servers, storage and applications are delivered to an organization’s computers and devices via the Internet.


    The NoSQL database, also called Not Only SQL, is an approach to data management and database design that’s useful for very large sets of distributed data. This database system is non-relational, distributed, open-source and horizontally scalable. NoSQL seeks to solve the scalability and big-data performance issues that relational databases weren’t designed to address.

    Apache Cassandra:

    Apache Cassandra is an open-source distributed database system designed for storing and managing large amounts of data across commodity servers. Cassandra can serve as both a real-time operational data store for online transactional applications and a read-intensive database for large-scale business intelligence (BI) systems.


    Amazon Simple Database Service (SimpleDB), also known as a key value data store, is a highly available and flexible non-relational database that allows developers to request and store data, with minimal database management and administrative responsibility.

    This service offers simplified access to a data store and query functions that let users instantly add data and effortlessly recover or edit that data.

    SimpleDB is best used by customers who have a relatively simple data requirement, like data storage. For example, a business might use cookies to track visitors that visit its company website. Some applications might read the cookies to get the visitor’s identifier and look up the feeds they’re interested in. Amazon SimpleDB gives users the option to store tens of attributes for a million customers, but not thousands of attributes for a single customer.

    Google BigTable:

    Google’s BigTable is a distributed, column-oriented data store created by Google Inc. to handle very large amounts of structured data associated with the company’s Internet search and Web services operations.

    BigTable was designed to support applications requiring massive scalability; from its first iteration, the technology was intended to be used with petabytes of data. The database was designed to be deployed on clustered systems and uses a simple data model that Google has described as “a sparse, distributed, persistent multidimensional sorted map.” Data is assembled in order by row key, and indexing of the map is arranged according to row, column keys, and timestamps. Here, compression algorithms help achieve high capacity.


    MongoDB is a cross-platform, document-oriented database. Classified as a NoSQL database, MongoDB shuns the traditional table-based relational database structure in favor of JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster.

    MongoDB is developed by MongoDB Inc. and is published as free and open-source software under a combination of the GNU Affero General Public License and the Apache License. As of July 2015, MongoDB is the fourth most popular type of database management system, and the most popular for document stores.


    Apache HBase (Hadoop DataBase) is an open-source NoSQL database that runs on the top of the database and provides real-time read/write access to those large data sets.

    HBase scales linearly to handle huge data sets with billions of rows and millions of columns, and it easily combines data sources that use a wide variety of different structures and schema. HBase is natively integrated with Hadoop and works seamlessly alongside other data access engines through YARN.


    Neo4j is a graph database management system developed by Neo Technology, Inc. Neo4j is described by its developers as an ACID-compliant transactional database with native graph storage and processing. According to, Neo4j is the most popular graph database.

    Couch DB:

    CouchDB is a database that completely embraces the web. It stores your data with JSON documents. It accesses your documents and queries your indexes with your web browser, via HTTP. It indexes, combines, and transforms your documents with JavaScript.

    CouchDB works well with modern web and mobile apps. You can even serve web apps directly out of CouchDB. You can distribute your data, or your apps, efficiently using CouchDB’s incremental replication. CouchDB supports master-master setups with automatic conflict detection.

    Key Takeaways

    Data has intrinsic value. But it’s of no use until that value is discovered. Equally important: How truthful is your data and how much can you rely on it?

    Today, big data has become capital. Think of some of the world’s biggest tech companies. A large part of the value they offer comes from their data, which they’re constantly analyzing to produce more efficiency and develop new products.

    Recent technological breakthroughs have exponentially reduced the cost of data storage and compute, making it easier and less expensive to store more data than ever before. With an increased volume of big data now cheaper and more accessible, you can make more accurate and precise business decisions.

    Finding value in big data isn’t only about analyzing it (which is a whole other benefit). It’s an entire discovery process that requires insightful analysts, business users, and executives who ask the right questions, recognize patterns, make informed assumptions, and predict behavior.

    The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smart decision making. When you combine big data with high-powered analytics, you can Align business-related tasks.