The discovery of oil over a century-and-a-half ago revolutionized the world. If a parallel has to be drawn in the current age of technological revolution, today’s oil is data. Unlike oil, however, data creation is seemingly infinite. Data was, and has been, always critical for organizations, and data creating business value is not a new idea; however, with increased volume-variety-velocity of data it has gained more importance. A change in approach is inevitable, and probably much needed more than before. Through analytics, organizations can mine data for valuable insights. These insights are used to build new products, enhance offerings, know customer preferences, among many other uses. Effective usage of data has become the key differentiator to beat the competition in the market. However, with increasing data being generated, if they are not analyzed quickly, they lose their shelf-life. Organizations need to act fast. The blend of unprecedented exponential data growth with the rapid development of technologies capable of storing and processing this information is transforming the way enterprises run their businesses. Realizing the value of data has increased over years and so has the associated risk of managing this data. The digitally-defined world is continuing to generate huge volumes of data and this growth has posed a question to organizations – Do you Dare to Use Me?
This need of gleaning intelligence from data and translating that into business advantage, increasing digitization of business activities, newer sources of information and probable usage of cheaper equipment brings us into a new era -- Replace or Be Replaced.
There is a need to develop or use specialized technological solutions that can store, process, and analyze this vast amount of data being generated – in near real-time.
Traditional solutions like existing RDBMS, inexpensive tapes and storage solutions for long-term archiving, are being replaced with solutions that offer capabilities to store more data online, allow streaming, and support agility and dynamism, such as big data technologies and NoSQL data stores.
Mobile devices, social media, online sources, electronic communication devices, instrumented machinery and, of course, ‘we the individuals’, are all walking data generators; in fact, data consumers have now become more like data generators.
Well! Data was always critical, however now businesses are interested in any/every data being generated and would like to work on all of it to uncover any hidden treasure in the form of insights to make better decisions.to Google’s Director of Research, Peter Norvig, has well said, “We don’t have better algorithms, we just have more data.”
Big Data and related solutions promise and threaten to end legacy technologies at many big companies as IT modernization initiatives gain pace and cost-savings add to the concern. Emphasis is being laid on the technology solutions that are tied to big data. Companies are not only replacing legacy technologies in favor of open source solutions like Apache Hadoop, they are also replacing proprietary hardware with commodity hardware, custom-written applications with packaged solutions, and decades-old business intelligence tools with data visualization. This new combination of big data platforms, projects, and tools is driving new business innovations and how businesses grow.
Interesting Facts and Statistics:
As per IDC (International Data Corporation), worldwide revenues for big data and business analytics will grow from nearly $122 billion in 2015 to more than $187 billion in 2019.
Organizations able to take advantage of the new generation of business analytics solutions can leverage digital transformation to adapt to disruptive changes and to create competitive differentiation in their markets - Dan Vesset, Group Vice President, Analytics and Information Management.
There is little question that big data and analytics can have a considerable impact on just about every industry - Jessica Goepfert, Program Director, Customer Insights and Analysis.
Services-related opportunity will account for more than half of all big data and business analytics revenue, within IT Services generating more than three times the annual revenues of Business Services.
Software will be the second largest category, generating more than $55 billion in revenues in 2019.
Nearly half of these revenues will come from purchases of End-User Query, Reporting, and Analysis Tools and Data Warehouse Management Tools. Hardware spending will grow to nearly $28 billion in 2019.
Large and very large companies (those with more than 500 employees) will be the primary driver of the big data and business analytics opportunity, generating revenues of more than $140 billion in 2019.
Apache Hadoop: an open-source software framework for distributed storage, distributed and parallel processing of very large datasets on commodity machines emerged a decade ago as an effective solution to deal with many of these emerging challenges. Various vendor-specific distributions, such as Cloudera, Hortonworks, IBM Big Insight, MapR followed the open source solution and have changed the way enterprises store, process and analyze data. Apache Hadoop, well-known as “Hadoop” and its ecosystem, has given organizations what they have been desperately looking for their data-related needs.
The Apache Hadoop ecosystem defines a mechanism for storing large data across systems using its logical, scalable, and fault-tolerant storage layer Hadoop Distributed File System (HDFS), and provides a framework for the distributed processing of large data sets across clusters of computers using simple programming models. The paradigm has shifted from scaling up to scaling out solutions. The capability to scale out (i.e. horizontally scale to thousands of machines, each offering local computation and storage) offers organizations a perfect solution for their ever-growing data. The open scale-out architecture with the usage of cloud adds to the possibilities. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
With its huge impact in the big data industry, Hadoop has helped organizations in gathering better insights on data to further growth. With its unbeatable data processing capability and utilization of batch processing, it has redefined big data analytics.
There has been much talk that the decade-old Hadoop is losing its edge as companies increasingly adopt newer technologies such as cloud; however, adoption of cloud platforms is more of a complement to a solution rather than a threat. Will learning Hadoop still make sense?
Here are some key insights about Hadoop for 2018 to answer all your questions.
Hadoop is still integral to big data
With newer solutions like the cloud, organizations may be moving much of their data on to the cloud, but most of it is done using HDFS at first or in a hybrid (on-premise cloud) integrated setup with clouds being able to integrate Hadoop. Its adoption has only increased. Some newer technologies, such as Spark, may even be replacing Hadoop’s most mature processing and programming model-MapReduce to a certain extent but that does not mean enterprises have forgotten the value of Hadoop. Real-time processing is gaining significance, however, batch processing, a need of fault tolerance and scalable storage still hold their grounds in maximum use cases. Spark can work in its own standalone dedicated cluster; however, this would again rely on the durability of machines and face blockages when it comes to failures or massive processing. Leveraging the benefits of the Hadoop platform, such as distributed storage and processing layer, can’t be ignored.For example, Spark does not have a built-in data management system like Hadoop does, which makes it a more useful data management platform. Organizations are seeing value in using a combination of newer and older but well-established technologies to derive maximum benefit from big data analytics. And, going forward organizations are likely to increase using this mixed methodology. Learning Hadoop is your first step into the world of BIG DATA.
Complement Hadoop with other technology
We spoke about how organizations are using a combination of technologies to maximize efficiency, increase productivity, and reduce costs. In the context of Hadoop, some of the newer technologies, such as Spark and Scala, need to be talked about here. Spark can be deployed on the same hardware of Hadoop and can use its resource management layer – Yarn apart from this Spark can process the data stored in HDFS (Hadoop Distributed File System). So, in order to store the huge amount of data, you can use HDFS and to process or analyze this data, you can use Spark. Scala is a general-purpose programming language providing support for functional programming and a strong static type system. Spark, which is written in Scala, allows users to use the conciseness of Scala and its scalability on JVM thus benefiting in terms of productivity and high performance. Scala provides better performance compared to other languages like Python or R, and less complexity as is in Java or C++. Scala’s usage, while working on Spark, ensures a well-tuned compute cycle and memory efficiency thus allowing users to build scalable applications -- one of the primary requirements of organizations intending to process data for various use cases.
Hadoop is evolving, too!
In late 2017, Apache released Hadoop 3. With this, Hadoop has greatly improved in efficiency, scalability, and reliability. Some of the new features include HDFS erasure coding, a preview of YARN Timeline Service version 2, and enhancements performance in cloud storage systems. With HDFS erasure coding, Hadoop allows for more compact, RAID-like storage, so users now have better ways to manage the lifecycle of their data.
$87.14 billion market size by 2022
If you still had any doubts, then maybe these numbers can allay them. In a November 2017 study by Zion Market Research, it is estimated that the market size of Hadoop is set to grow to $87.14 billion by 2022. That’s a compounded-annual growth rate of nearly 50 percent over the next four years. The report notes that, “The Hadoop market growth is driven by increasing demand for the big data coupled with the growing volume of structured and unstructured data. Another important factor that is expected to propel the market growth of Hadoop is escalating demand for effective and faster accessibility of data among various industries like healthcare, banking & finance, manufacturing, biotechnology & defense.”
Adopting Hadoop does not come without its challenges. One among them is finding people with the right Hadoop skillset. McKinsey estimates that by 2018, there will be a shortage of 1.4 -1.9 million Hadoop Data Analysts in the US alone. The trends clearly indicate that Hadoop is going strong – and is likely to continue, despite newer technologies emerging alongside. In fact, the newer technology may augment the potential of Hadoop’s capabilities.
This presents an opportunity for many to upskill and build a successful career in data analytics by learning Hadoop. With the right Hadoop skills, one can find a wide range of jobs, such as Hadoop architects, administrators, developers, and data scientists. Mindteck Academy’s live, instructor-led Hadoop, online course prepares experienced and rookie professionals for in-demand roles at data-driven enterprises around the globe. The first half of the course begins with an introduction to Big Data concepts and its application in the real world. Then the lectures move on to advanced topics, such as the Hadoop Distributed File System, batch/parallel processing using MapReduce, and various frameworks defined in the Hadoop Ecosystem (i.e. YARN, HBase, Pig, Hive, Oozie and Zookeeper). This course, taught by industry experts, gives the participant a comprehensive picture on big data and the Hadoop ecosystem. By learning the combination of Hadoop, Spark and Scala you will be better equipped to work on big data analytics projects, resulting in a higher-flying career with lucrative opportunities.
From Apple Watch reminders to breathe, to advertisements for recent purchase considerations appearing in your feed, Big Data, AI and Data Analytics has moved beyond “the next big thing.” Big Data – a term originally coined for data that has various attributes such as volume, variety, validity and volatility to name a few – has fundamentally changed business practices across every company. It is now ubiquitous across every function and department of enterprises both large and small. The complex challenge for enterprises to capture, understand, analyze, interpret and harness big data in a meaningful and relevant way is led by data scientists, business intelligence analysts, data architects, and developers.
Facts about Big Data Careers
What’s Hadoop got to do with it?
Hadoop, as a framework, has proven its worth in the Data Analytics revolution because of its unique feature set – cost effectiveness, flexibility and scalability. Big Data Hadoop is synonymous to a golden goose for IT professionals. The more proficient you are in big data related technologies like Hadoop and its ecosystem and real time in-memory computing framework Spark, the more likely it is that you can command higher pay checks.
To be a part of the Big Data revolution, here are some basics:
So why add Spark with Scala?
Apache Spark is a second-generation Big Data toolset, providing both batch and streaming processing capabilities for faster data processing. Spark works seamlessly with Hadoop, leveraging the same hardware and resource management layer – Yarn. Apart from this, Spark can process the data stored in HDFS (Hadoop Distributed File System). Since Spark projects are developed using Scala, becoming an expert in Scala is imperative.
There is currently a shortage of skilled resources who can work in the Big Data space -- Big Data Developer, Data Analysts, Big Data Consultants, Big Data Platform Engineers, Architects, Solution Analysts, Data Scientists and many other roles. All of these positions require profound knowledge of Big Data tools and technologies, i.e. Hadoop ecosystem and its components, with an emphasis on in-memory and real-time processing frameworks, such as Spark.
Mindteck Academy’s live, instructor-led Hadoop, Spark and Scala course is a structured program covering detailed learning on the Hadoop ecosystem components, curated and taught by an industry expert. Mindteck Academy’s rigorous course prepares students for enterprise-level positions, broadens their skillset, and increases their value to an organization.
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