Reasons to learn Apache Spark


Spark is an evolutionary change since the companies are creating a large amount of data rapidly, and along with this, it is necessary to work on the analysis of the data and produce some useful insights for the betterment of the business. Spark is a platform that provides speedy data analysis, and it also provides batch and streaming capabilities, which makes Spark a preferred choice.

It is time to get the Spark course and upgrade your vital data skills, but why?

In 2015, O’Reilly conducted a survey that revealed that the people who use Scala and Spark strongly correlated with the change in their salary. People with Spark skills added $11,000 to their median wage, while the Scala skills had an impact of $4,000 to the bottom line. Using 10 of the most prominent Hadoop tools, Spark developers earn the highest salary among the programmers.

Apache spark framework provides memory computing, rendering performance benefits to the users, and hence developers are tapping big data with Apache Spark. Apache Spark provides an excellent platform for data processing since it is implemented in the Scala programming language. More than 200 companies are using Spark worldwide, with over 750 contributors making Spark the most significant and fastest-growing big data community. Let us look directly into the reasons to learn Apache Spark.

Make use of existing significant data investments.

The companies can use Spark over any existing Hadoop clusters; hence Spark has no limitations on investing in new computation clusters. Hadoop MapReduce can run on HDFS and the YARN, and therefore Apache Spark can run on it. Companies are on the verge of hiring a large number of spark developers since Spark is highly compatible with Hadoop and also, the companies do not wish to re-invest in computing clusters.

Learn Apache Spark to get increased access to big data.

Many opportunities for Big data exploration are going to be opened up by Apache Spark, making it easier for companies to solve the problems related to big data. Spark is not just a hot technology among data engineers, but also many data scientists consider working with Spark. To craft an analytics workflow, the developers can write code using Python, R (SParkR), Java, etc.

Unlike Hadoop MapReduce, Spark can store the data resident in memory, which helps speed up the machine learning workload. Hence, the data scientists are exhibiting interest in working with Apache Spark. Spark is growing exponentially in the big data ecosystem.

IBM also announced that it would educate more than a million data engineers and data scientists on Spark. Getting a big fat salary is indeed important, even if you can achieve your salary goals if you are a trained Spark developer. You can surely become one with the help of the Spark course.

Pace up with growing enterprise adoption.

Monte Zweben, the CEO of Splice Machine, said that “Spark will reinvigorate Hadoop, nine out of every ten projects on Hadoop will be Spark-related projects.”

Companies are preferring the adoption of various big data technologies that complement and are adjacent to Hadoop-Spark. Apache Spark is no longer just a component of the Big Data ecosystem, but it has also become the most preferred big data technology for companies worldwide.

Matei Zaharia, the originator of the Apache Spark, said that “Spark provides dramatically increased data processing speed compared to Hadoop and is now the largest big data open-source project.

A Spark adoption survey revealed that the community which has the most significant number of contributors as compared to any other open source project managed by Apache Foundation is the Apache Spark.

There is an increasing need for Hadoop and Spark SQL, the two big data tools which are supposed to be combined with supporting the BI workload. Among the total Apache Spark adopter companies, 68% of them use Spark to render the support for BI workload. There is a lucrative opportunity for big data developers who are skilled in Spark and Hadoop.

Apache Spark is set to witness an increasing demand for Spark Developers.

Spark is the most fantastic alternative for MapReduce (outside or within the Hadoop framework), and it also has the potential to eclipse the Hadoop, Spark’s enterprise adoption is increasing rapidly. No doubt what, Apache Spark requires technical expertise in the object-oriented concepts to program and run. It opens up various job opportunities for those who have practiced Apache Spark and are getting hands-on training.

In the industry, the shortage of Apache Spark skilled and trained individuals is leading to a large number of open jobs and various contracting opportunities for prominent data professionals. It strongly suggests the need for the Spark course, which guarantees excellent guidance and a chance to work on industry level projects.

Learning Apache Spark will open up many opportunities for the person who wishes to work at the forefront of big data technology. The Spark course is the way to bridge the skill gap getting a job related to data or finding a job position as a Spark Developer since it will provide training and hands-on experience with the help of working on several projects.

Learn Apache Spark to keep your pocket happy.

Companies are ready to bend the recruiting rules, provide flexible working time, and offer attractive benefits for getting a skilled Apache Spark developer. It shows the demand for trained and certified Spark developers.

O’Reilly also said that the data engineer who has experience in Apache Spark earns the highest salary. The data analyst and data engineers having critical data skills like Hadoop earn around $1,20,000 per year, whereas an average IT tech earns around $89,500 per year. Professionals with Apache Spark and Storm skills are touching about $1,50,000 yearly salary, and the average salary of data engineers is approximately $98,000.

People with a burning desire to lay their career path in Big Data and earn a significant amount should learn Apache Spark since big data has a promising and flourishing future.

About the author


Editorial Staff

Add Comment

Click here to post a comment