Big Data Analytics is a field that has shown signs of growth in the smallest period of time. As a result of the data explosions taking place in every part of the world, organizations are just too keen on leveraging it for adding value to their business.
Therefore, analyzing this data and taking insights from it in order to help businesses grow is the fastest growing skill that the tech world has seen so far. Conventional data analysis is not able to keep up with the emergence of Big Data. Big Data is fundamentally enormous data, both unstructured and structured.
There is an unexplainably huge demand for professionals with big data analytics skills as every business wants to extend its reach in a multitude of markets by evaluating tons and tons of important data and understand more about their functions and the needs of their customers.
By taking support from Big Data experts and big data analytics tools, any company can stand out from the competition and establish itself as the market leader of its industry. But, it is not easy becoming a big data analyst, especially with the advent of a myriad of techniques and tools that the tech domain is seeing these days.
The great news for people aspiring to be big data analyst is that the part where you make an analysis is the same, irrespective of the sizes and shapes of datasets that you come across. The thing that you need the most to survive in the big data field is the capability to withdraw pertinent information out of the gigantic amounts of data that is being processed all day long. This is where contemporary technology has to shake hands with conventional analytics.
1. Programming
While a conventional data analyst could be able to perform his/her job without having a full-fledged knowledge of programming, a big data analyst has to be well-versed with coding. The primary reason for this need is that big data analytics is still in its cradle phase.
What I mean to say is that there are not many standard procedures that have been set around the giant and complex datasets which big data analysts have to deal with.
The languages that one needs to learn for undertaking big data analytics are Python, R, C++, Java, Ruby, Hive, SQL, SAS, MATLAB, SPSS, Julia, Weka, and Scala. A big data analyst cannot perform their job without a lack of understanding of basic programming languages. So, at the minimum, they are required to be familiar with Python, R along with Java.
2. Data Warehousing
Also, to be a part of the Big Data Analytics dominion, one needs to have experience of working with relational as well as non -relational database systems.
The most well-known relational databases are Oracle, Mysql, and DB2. While the most in-demand non-relational databases are: NoSQL: Hbase, MongoDB, HDFS, CouchDB, Teradata, Cassandra and so on.
3. Computational frameworks
A detailed understanding of frameworks like Apache Storm, Apache Spark, Apache Flink, Apache Samza along with the classic Hadoop and MapReduce. These technologies are incredibly helpful in carrying out Big Data processing.
4. Quantitative Aptitude as well as Statistics
There is a profound use of technology while performing Big Data processing, however, it is essential to have a deep knowledge of linear algebra and statistics to execute any type of processes.
Statistics can be seen as a fundamental building block of big data analytics, therefore, having an understanding of key concepts like a probability distribution, summary statistics, and random variables is beneficial for big data analysts.
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