Articles

Good old big data tools that still work

by Angela Hooper Senior Content Writer

Time flies faster in the tech world. You stop paying attention for a while and suddenly find yourself struggling for validation in the middle of a paradigm shift. But let us not be all philosophical and focus instead on some big data tools that might have lost their place amidst the buzz but are very relevant and supremely active in the industry, because you have better things to do than reading stories of the future.

Hadoop to start with


This is really the tool that really made a real noise in the tech fraternity. The cute little elephant in their logo is no less popular than the fuzzy brain images they use to suggest AI. HDFS, that is Hadoop distributed file system, was among the first tools that made data storage affordable enough for the small and midsize enterprises. Before HDFS data storage was a nightmare even for the big players. Now, thanks to cloud computing HDFS is somewhat out of the map and Spark has taken the spot of Mapreduce in the capacity of data processing. Nevertheless there is still significant demand for Hadoop operators, more because they are a dying breed of personnel. A lot of large conglomerates have continued working with Hadoop tools. Search for Hadoop jobs and you will know.

Spark is still bright


Spark brought a revolution of sorts for big data analytics users. Its in-memory analysis made data processing faster, almost real-time. The advent of neural networks has somewhat overshadowed Spark’s speed, but that does not make it an obsolete skill to have. There is still ample demand for Spark skills. In fact it is one of the most important data analytics tools to learn.

There is no stopping SQL


SQL works on very simple principles. That is why this data querying tool has never lost its utility. Much like MS excel, this old school piece of tech is used by thousands of people on a daily basis in the fields of business intelligence, data engineering, and analytics.

R you ready for some statistical computing


Yes, despite the disruptive presence of Python as an all purpose language, R has maintained its stature as a supreme tool for statistical computing. Data science professionals working with R are satisfied and do not bother moving to anything else. The flexibility of R with various data sources and formats is incredible. It works smoothly in tandem with tools like Excel and Tableau.


If you are an analytics enthusiast trying your luck in Bangalore, the technical hub of India, you must consider undergoing big data training in Bangalore to learn these tools to make a smooth transition into a career in analytics.


Sponsor Ads


About Angela Hooper Innovator   Senior Content Writer

24 connections, 0 recommendations, 75 honor points.
Joined APSense since, July 19th, 2016, From Houston, United States.

Created on May 3rd 2020 02:30. Viewed 238 times.

Comments

No comment, be the first to comment.
Please sign in before you comment.