Career in Hadoop

Introduction to Career in Hadoop

Hadoop is not a mere framework in the Big Data world. It has a wide ecosystem with an umbrella of related technologies. For the same reason, a career in Hadoop is promising. If you have a good understanding of Hadoop fundamentals, it will be a foundation for a great career in Hadoop.

Education Required for Career in Hadoop

Like many emerging data technologies, Hadoop doesn’t demand any specific educational background as such. Around half of Hadoop, developers are from non-computer science backgrounds like Statistics or Physics. So it is clear that the background is not a hindrance to entering the world of Hadoop provided you are ready to learn fundamentals. There are good online courses covers Hadoop  – the one from eduCBA is the best example – master-apache-Hadoop

Further, if you want to move deeper into a specific area of Hadoop cluster management or data modelling in Hive materials on each specific topic available as online courses and textbooks, most of the time Hadoop clusters will be set up in a cloud vendor like AWS or Azure. So get familiar with any cloud vendor of your choice will help a lot. Hadoop service from AWS is called EMR.

The popular specialization includes :

• Spark – Scalable in-memory data processing engine
• HBase – No SQL Database on top of HDFS
• Beam – Streaming first approach data processing
• Pig – Data transformation(ETL) scripting
• Hive – Data warehousing
• Mahout, Spark MLlib – Scalable Machine Learning on Hadoop
• Apache Drill – SQL engine on Hadoop
• Flume, Sqoop – Data Ingesting Services
• Solr & Lucene – Searching & Indexing

Career Path in Hadoop

As per Stack Overflow Survey 2017 results, Hadoop is leading in the most popular and most loved framework in Big Data space ( Survey Link). This is possible only because people from different IT perspective found Hadoop a potential career path and want to switch.

Whatever your current role is IT, there will be an easily adaptable switch to a career in the Hadoop world. Some popular examples –

• Software Developer(Programmer): Hadoop Data Developer who deals with different Hadoop abstraction SDKs and derive value from data.
• Data Analyst: So you are proficient in SQL.Huge opportunity in Hadoop to work on SQL engines like Hive or Impala
• Business Analyst: Organisations trying to become more profitable using massively collected data and a business analyst role is crucial in this.
• ETL Developer: If you are working as a traditional ETL developer, you can easily shift to Hadoop ETL using Spark tools.
• Testers: There is a huge demand for testers in the Hadoop world. By understanding the fundamentals of Hadoop and data profiling, any testers can switch to this role.
• BI/DW professions: Can easily switch to Hadoop Data architecting to Data modelling.
• Senior IT professionals: With a deep understanding of the domain and existing challenges in the data world, a senior professional can become consultants by gaining knowledge of how Hadoop trying to solve these challenges.
• There are generic roles like Data Engineers or Big Data Engineering, who is responsible for implementing solution mostly on top of Cloud vendors. By gaining knowledge of data components of the cloud provides, this will be a promising role.

Job Positions

Hadoop ecosystem offers a variety of career path.

• MapReduce Developer: This basically a Java developer role who also understand how Hadoop systems work internally. There is an abstraction like Hive or Pig available still MapReduce jobs are necessary for high performing systems. MapReduce developers are the one who understands a system in and out and paid really high.
• Hadoop Administrators: These are people responsible for keeping the Hadoop cluster healthy and performing. This may include typical Hadoop administrator tasks like regular system health checks, but most tasks are needed to understand Hadoop system architecture.
• Devops: Deploy new system components and other development-related changes in Hadoop cluster. The responsibility of this role varies a lot and depends on the culture of an organization.
• Data Developer: Data processing on top of Hadoop. This one of the most popular roles in the Hadoop ecosystem.People from SQL or analytics background best fit for these roles. Mostly work on a high-level abstraction of Hadoop like Hive or Pig.
• Data security admin: Data is the most valuable assets, and securing it is most important. Security admins ensure industry-standard policies and best practices to protect data, understanding a system’s limitation.
• Data Visualizer: Handle next-generation visualization tools which allow dynamic data slicing and aggregation with in-memory data caching
• ETL Developer: Transform data for data quality improvement or as per business logic using Hadoop ecosystem tools. ETL process might be streaming or batch.
• System Architect: Design high performing systems considering data availability and durability in a cost-effective manner. Depends heavily on Hardware provider.
• Data Architect: Apart from traditional Logical/Physical design of data, a lot of things like column encoding, denormalization, partitioning design etc. will be a responsibility of data architect.

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Salary

An average salary of a Software developer in the US is $90,956 per year while the average salary of Hadoop developer is a way higher – $118,234 per year ( As per Indeed.com – indeed.com )

Salaries of Hadoop developer in top companies in the US (Ref: indeed.com)

Apple	$147,573 per year
Wipro	$110,553 per year
HERO.jobs	$158,715 per year
MBCAA	$133,422 per year
Ventures Unlimited Inc	$130,000 per year
Nityo Infotech Services Pvt. Ltd.	$128,633 per year
NORTH STAR	$126,370 per year
PRI Technology	$121,396 per year
NITYO INFOTECH	$116,909 per year
HortonWorks, Inc	$110,710 per year

Career Outlook

Hadoop ecosystem is getting diverged a lot to meet a change in business needs. As data generated is increasing exponentially and more and more organizations become data-driven, the Hadoop system’s relevance only going to increase.

Some of the notable trends :

• Shift from batch processing to stream first data processing approach using Spark and Beam
• More real-time Machine Learning model applied to real-time data using Spark ML.
• Decoupled SQL engines from data storage like Presto on top of S3 for ad-hoc analysis on top of the data lake.
• Columnar MPP databases like AWS Redshift for quick data access

As a fundamental aspect of Big Data processing lies on fault-tolerant distributed and horizontally scalable systems, which Hadoop well implements, Hadoop will continue as a leading ecosystem for data processing.

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