Sunday, May 15, 2016

5 Reasons to Learn Apache Spark

5 Reasons to Learn Apache Spark

1) Learn Apache Spark to have Increased Access to Big Data

Apache Spark is opening up various opportunities for big data exploration and making it easier for organizations to solve different kinds of big data problems. Spark is the hottest technology now, not just among the data engineers but even majority of data scientists prefer to work with Spark. Apache Spark is a fascinating platform for data scientists with use cases spanning across investigative and operational analytics.
Data scientists are exhibiting interest in working with Spark because of its ability to store data resident in memory that helps speed up machine learning workloads unlike Hadoop MapReduce. Apache Spark has witnessed continuous upward trajectory in the big data ecosystem. With IBM’s recent announcement that it will educate more than 1 million data engineers and data scientists on Apache Spark – 2016 is definitely THE year to learn Spark and pursue a lucrative career.

2) Learn Apache Spark to Make Use of Existing Big Data Investments

After the inception of Hadoop, several organizations invested in novel computing clusters to make use of the technology. However, Apache Spark does not pose any limitations on investing in new computing clusters as organizations can use Spark on top of the existing Hadoop clusters.

Spark can run on Hadoop MapReduce as it can run on YARN and on HDFS. With high compatibility of Spark with Hadoop, companies are on the verge of hiring increased number of Spark developers as they do not have to re-invest on computing clusters because it can be integrated well with Hadoop. This also makes learning spark an added advantage for professionals with expertise in Hadoop skills.

3) Learn Apache Spark to pace up with Growing Enterprise Adoption

Spark will reinvigorate Hadoop, and in 2016, nine out of every 10 projects on Hadoop will be Spark-related projects. — said Monte Zweben, CEO of Splice Machine
With companies embracing the adoption of various adjacent big data technologies that complement Hadoop-Spark adoption rate is increasing exponentially. Spark is no more just a component of the big data Hadoop ecosystem but has become the go-to big data technology for enterprises across various verticals.
“Spark provides dramatically increased data processing speed compared to Hadoop and is now the largest big data open-source project.” said Apache Spark originator Matei Zaharia.
A recent survey on Spark adoption revealed that Spark community has had most of the contributions compared to other open source projects managed by Apache foundation. There is an increasing demand to support BI workloads using a combination of the two big data tools - Hadoop and Spark SQL.
The survey findings show that among Apache Spark adopters 68% of the companies are using Spark to render support for BI workloads. Spark’s clear value proposition is leading to an increased adoption rate by enterprises opening up lucrative opportunities for big data developers with Spark and Hadoop skills.
Big data predictions for 2016 expect Apache Spark to go its own way, creating a novel, vibrant ecosystem with popular cloud vendors releasing their individual Spark PaaS offerings.

4) Learn Apache Spark as 2016 is set to witness an increasing demand for Spark Developers

Spark’s enterprise adoption is rising because of its potential to eclipse Hadoop as it is the best alternative to MapReduce - within the Hadoop framework or outside it. Similar to Hadoop, Apache Spark also requires technical expertise in object oriented programming concepts to program and run- thus opening up job opportunities for those who have hands-on working experience in Spark. Industry-wide Spark skills shortage is leading to a number open jobs and contracting opportunities for big data professionals.
For people who want to make a career on the forefront of big data technology, learning apache spark now will open up a lot of opportunities. There are several ways to bridge the skills gap for getting a data related jobs and finding a position as a Spark developer. The best way is to take a formal training that provides hands-on working experience and helps learning through hands on projects.
According to the popular IT job portal,, a keyword search for the term “Spark Developer” showed 34617 listings as of 16th December, 2015.

5) Learn Apache Spark to make big money

Spark developers are so in-demand that companies are agreeing to bend the recruitment rules, offer attractive benefits and provide flexible work timings just to hire experts skilled in Apache Spark. According to, the average salary for a Spark Developer in San Francisco is $128, 000 as of December 16, 2015. statistics reveal that the average salary for spark developers in San Francisco is 35% more than the average salaries for Spark developers in US.

According to O’Reilly, data engineers who have experience with Apache Spark and Storm earn the highest average salaries. Several recent salary surveys found that data engineers and data analysts having big data skills like Hadoop are earning close to $120,000/year, when compared to the average IT tech salary of $89,450. Apache Spark and Storm skilled professionals are pulling close to $150,000 in yearly salaries, when compared to the total average salary of data engineers which is $98,000. People with a keen desire to grow their big data career and earn high salaries - must learn Apache Spark online now.

Get Started with Learning Apache Spark
As Spark continues to be used for interactive scale out data processing requirements and batch oriented needs, it is expected to play a vital role in the next generation scale out BI applications. Professionals need to undertake comprehensive hands-on training in Spark to become productive especially if they are newbies to Scala programming. It requires professionals to get comfortable with a new programming paradigm like Scala. However, one can also use Shark i.e. SQL on Shark to get started with learning Apache Spark.

No comments:

Post a Comment

Write a comment . .