Apache Spark and Scala (36 Blogs) Become a Certified Professional

5 Reasons to Learn Apache Spark

Last updated on Apr 28,2020 10.1K Views

Those who have been into Big Data probably know about Spark, popularly known as the Swiss Army knife of Big Data analytics. We have talked about the different features of Spark in our previous posts. For those who are new to Spark, it’s a cluster computing framework for data analytics that can handle almost all sorts of queries of all sorts of data types in a lightning fast speed. With the existing as well as new companies showing high interest in adopting Spark, the market is growing for it. Here are five reasons to learn Apache Spark which focalize as to why you should not keep yourself from learning this revolutionary new generation technology.

1# Integration with Hadoop

Spark can be integrated well with Hadoop and that’s a great advantage for those who are familiar with the latter. Technically a standalone project, Spark has been designed in a way to run on Hadoop Distributed File System. It can be straight-away got to work with MapR. It can run on HDFS, inside MapReduce. Having deployed on YARN, it can even run on the same cluster alongside MapReduce jobs.

Read more on Why Spark with Hadoop matters?

2# Meet the Global Standards

According to technology forecasts, Spark is the future of worldwide Big Data Processing. The standards of Big Data Analytics are rising immensely with Spark, driven by high speed data processing and real time results. By learning Spark now, one can meet the global standards to ensure compatibility between next generation of Spark applications and distributions by being a part of Spark Developer’s Community. If you think you love technology, contributing in the development of a growing technology in its growing stage can give a boost to your career. After this, you can stay up to date with the latest advancements that take place in Spark and be among the initial ones to build the next-generation of big data applications.

3# Fading MapReduce and Sparking Spark

Spark is an in-memory data processing framework, and is all set to take up all the primary processing for Hadoop workloads in future. Being way faster and easier to program than MapReduce, Spark is now among the top-level Apache projects, which has acquired the espousal of large community of users as well as contributors. Matei Zaharia, CTO, Databricks and one of the brains behind Apache Spark project puts forth Spark as a multi-faceted query tool that could help democratize the use of big data. He also projected the possibility of end of MapReduce era with the growth of Apache Spark.

4# Spark Already being used in Production

The number of companies that are using Spark or are planning the same has exploded over the last year. There is a massive surge in the popularity of Spark, the reason being its matured open-source components, and an expanding community of users. The reasons why Spark has become one of the most popular projects in Big Data are, the ingrained high-performance tools handling distinct problems and workloads, and a swift and simple programming interface in Scala, Java, or Python.

There are several reasons, as to why enterprises are increasingly adopting Spark, ranging from speed and efficiency and ease of use to single integrated system for all data pipelines, and many more. Spark being the most active big data project has been deployed in production by all major Hadoop as well as non-Hadoop vendors across multiple sectors, including, financial services, retail, media houses, telecommunications, and public sector.

5# Huge Demand for Spark Professionals

Spark is brand new and yet to completely spread out in the big data market. The use of Spark is increasing at a very fast speed among many of the top-notch companies, like NASA, Yahoo, Adobe. Apart from those belonging to Spark community, there is a handful of professionals who have learnt Spark and can work on it. This in turn has created soaring demand for Spark professionals. In such a scenario, learning Spark can give you steep competitive edge. By learning Spark at this point in time you can demonstrate the recognized validation for your expertise. This is what John Tripier, Alliances and Ecosystem Lead at Databricks has to say, “The adoption of Apache Spark by businesses large and small is growing at an incredible rate across a wide range of industries, and the demand for developers with certified expertise is quickly following suit”.

Got a question for us? Please mention them in the comments section and we will get back to you.

Related Posts

Apache Spark Lighting up the Big Data World

Apache Spark Ecosystem

Apache Spark with Hadoop-Why it matters?

Start your Training in Apache Spark & Scala Today!!

Upcoming Batches For Apache Spark and Scala Certification Training Course
Course NameDate
Apache Spark and Scala Certification Training Course

Class Starts on 4th November,2023

4th November

SAT&SUN (Weekend Batch)
View Details
1 Comment

Join the discussion

Browse Categories

webinar_success Thank you for registering Join Edureka Meetup community for 100+ Free Webinars each month JOIN MEETUP GROUP

Subscribe to our Newsletter, and get personalized recommendations.

image not found!
image not found!

5 Reasons to Learn Apache Spark