How to Become a Big Data Analyst?

Vinod Raju


January 20th, 2017

The reason aspirants are diving into Big data & analytics is obvious. The plethora of interesting technologies & tools surrounding it is enormous which is creating the buzz among both the aspirants and the industries thriving for it. But the BIGGER question is how to acquire the right skills to qualify as a Big Data Analyst?

The role of a Big Data Analyst is not just limited to analysis of raw data but it ranges from a data engineer’s involvement in ETL operations, working with reporting & visualization tools to machine learning algorithms. The job responsibilities of that of a data scientist and that of an analyst overlap quite often unless a data scientist is a mathematical or a statistical purist.

A smart way to acquire similar skillset is to engage in real time project training using the same set of tools and methodologies currently used in the industry.


Become a Big Data Analyst with our custom designed Big Data Specialization that trains you on Hadoop, Pig, Hive, Spark, Storm, MongoDB, and Cassandra.


What makes for a Big Data Analyst?

Skillset required for a Big Data analyst for ETL based roles involves technologies around HADOOP (map reduce, pig, sqoop, flume, hive, oozie etc). Real-time analytics relies on the technologies like STORM & KAFKA while SPARK, Scala & Python offer more scope in the areas of machine learning used for predictive modeling. The above set of tools and technologies can be leveraged with NoSQL databases which again can be useful in real time analytics. On the other hand, programming languages like R & Python offer a more customizable approach to problem-solving in the areas of data science incorporating machine learning algorithms.


One would easily get lost in the myriad of technologies and tools surrounding the Big Data without proper guidance.

Becoming a Big Data Analyst

A novice could start from Hadoop-Spark, move into real-time data analytics using Storm-Kafka & NoSQL databases, and migrate towards machine learning using Python-Spark.

Someone with prior experience in analytics especially with tools like SAS/R could start from learning data science with R and the migrate towards technologies like SPARK, Python and data science using machine learning.

The learning path which seems simplistic is quite exhaustive when we dig deeper. Each tool/technology in the learning path requires a meticulous approach with a good understanding of fundamentals backed by a strong hand on experience in solving real time case studies, all of which is designed by subject matter experts in the industry while designing the courses.


Suggested Read

Launch Alert: Introducing the Jigsaw Big Data Analytics Course

5 Tips for Non-Programmers Learning Analytics


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