The Global Hadoop Market is growing at a CAGR of 54.9% and will be worth $13.95 Billion by 2017 – marketsandmarkets.com
Hadoop professionals are in much demand today. For those not in the loop, Hadoop is a massively parallel distributed processing framework that is deployed exclusively for working with Big Data. Today huge swathes of data routinely crop up and most of it is unstructured.
Do you know why Hadoop is so much sought-after?
Though Hadoop is extremely vital to enterprises around the world, there aren’t enough qualified professionals.
As per job search portal indeed.com there is a huge mismatch between the demand and supply of quality Hadoop professionals. This is good news for people with the right set of Hadoop skills. Here’s what you can expect in the Hadoop domain on the jobs front:
- Average Hadoop Developer Salary in the US is around $112,000 which is 95% higher than average salaries for all job postings nationwide.
- The Top Hadoop Salary goes to the Hadoop Administrator at $123,000 per annum.
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Using the Hadoop technologies it is possible to derive real-time analytics from vast amounts of data from the web, social media channels, video, audio, log data and even machine-generated data. Today top companies are relying heavily on Hadoop ecosystem and Hadoop-based applications to make sense of clickstream data, customer buying behavior, personas data, digital content processing and a whole host of data streams.
In a recent survey conducted by Syncsort, more than 250 highly placed professionals in the Big Data Hadoop domain that included IT Developers, Managers, Business Intelligence, data architects, analysts and scientists participated. Some of the key insights from that survey were as below:
- More enterprises are going into full adoption rather than just experimentation with Hadoop
- Converting from other platforms to Hadoop is top priority
- Hadoop is being deployed for advanced use cases cutting across industry sectors.
Choosing the Right Hadoop Skill sets
The two most important components of Hadoop are HDFS (Hadoop Distributed File System) and MapReduce. HDFS as the name suggests lets you store, distribute, split and manage huge amounts of data sets. Efficient use of HDFS will help you avoid the trouble of moving data back and forth on the huge network. HDFS lets you not only store but stream data at high bandwidth to wherever it is needed.
Learn more about HDFS in this Insightful Tutorial Now!
The MapReduce distributed processing will let you process huge amounts of unstructured data on commodity hardware clusters in which each cluster has its own storage. The MapReduce process includes the Mapping process wherein the work is sent to various cluster nodes and Reducing process wherein the results from Mapping are reduced into a logical manner.
Learn more about MapReduce in this comprehensive Tutorial Now!
Then there is another important component called YARN (Yet Another Resource Negotiator) which is part of the Hadoop 2.0 ecosystem. YARN is a large-scale distributed operating system that is used for data applications. Using YARN it is possible to let the scheduling of tasks and managing the resources to work independently. This way it is possible to deploy a more sophisticated processing approach.
Importance of the Hadoop Ecosystem
The Hadoop Ecosystem is a set of technologies that speeds up the process of Hadoop, helps to work with diverse database solution and also come with improved capabilities. The Hadoop Ecosystem includes
HBase – it can efficiently work on top of HDFS and it is a NoSQL columnar database. It is widely used for working with sparse data and also data that is stored in a columnar model.
Hive – it is a data warehouse infrastructure that works on top of Hadoop. It fully supports MapReduce and supports HiveQL language. You can easily integrate with various analytics platform and perform faster querying and indexing.
Spark – Spark is a high-speed processing that can be up to 100 times faster than MapReduce. It extensively uses the in-memory rather than fetching data from the disk.
Check this blog post to learn about the comparison between Spark and MapReduce!
Flume– Flume collects the data from the server and upon aggregation does the transferring of data to Hadoop.
Mahout– Mahout works extensively in machine-learning scenarios with its huge library. It collates algorithms that can help to smoothly work on the MapReduce model. It is also able to work on the Apache Spark system.
Sqoop– Sqoop is a tool that lets you convert data from the various databases and move it over to the Hadoop framework for processing and analyzing.
Check out the major organizations that are currently hiring Hadoop professionals
Learning to program for the Hadoop framework:
Hadoop needs the full support of various programming languages. This could be core Java programming or advanced programming skills like Python or even R. You can write applications for Hadoop using any of the objects-oriented programming languages. Plus some of the Hadoop components support their own programming languages like Pig supports Pig Latin and so on.
Get a head start in a software language of your choice in this extensive Programming Training!
The most important programming language for Spark is Scala. So having proficiency in Scala means you can deploy high-speed processing capabilities for Apache Spark. It is a fact that Spark is being rapidly deployed by major enterprises around the world due to its sheer speed, efficiency, scalability and versatility.
Pig is a high-level programming language that can analyze huge amounts of data. One of the core strengths of Pig is that it can work in an extensive parallelization mode to get the task done faster. Learning Pig Latin gives you a huge upside since this programming language abstracts Java MapReduce and converts it into a high level programming.
Having expertise in NoSQL is very useful since most of the data that we are dealing with today involves working with unstructured ones that cannot be stored in a tabular relation format. NoSQL’s popularity and importance has surged with the rise of Hadoop framework since it is possible to process large amounts of data and that too in real-time using NoSQL capabilities.
Find out more in this all-in-one NoSQL Combo Training Course to get a distinctive lead now!
Check this Survey Report to Learn how the Right Hadoop Skills can Take Your Career to the Next Orbit!
An opportunity like this comes once in a lifetime. Hadoop is today on the cusp of a major boom and those who are prudent enough to get on the Hadoop bandwagon will be amply rewarded indeed!
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