Reasons to learn Apache Spark

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Reasons to learn Apache Spark

Big Data and Analytics are changing how firms make educated market-oriented decisions, develop optimally promising client segment targeting strategies, and stay protected from market hiccups and economic volatility. Mining information locked in massive data volumes generated online or from other connected sources impacts these capacities.

The Apache Spark interface can safely process large amounts of data. Spark allows for smooth programming of data clusters and allows for fault tolerance and data parallelism. It means that this open-source platform can process massive datasets quickly. In terms of more robust and more advanced data handling, storing, evaluating, and retrieving capabilities, Apache Spark outperforms Hadoop. The Spark framework includes modules for machine learning, real-time data streaming, textual and batch data, graphics, and more, making it perfect for various industries. You can learn in-depth about the Apache spark in the spark course. 

What are the Benefits of Learning Apache Spark?

Data science provides unrivaled opportunities if you wish to advance in your profession. You may learn to analyze patterns and make conclusive fact-based assumptions using Apache Spark training. Also, if you’re working as part of a team to corner your specialized market, you’ll need to obtain specific insights into how the market is changing.

There are numerous benefits to learning this framework-language combo as an aspirant or exposing your company’s selected workers to it.

1) Learn Apache Spark to Gain More Big Data Access:

Spark is the hottest technology right now, not just among data engineers but also among the majority of data scientists. Apache Spark is an intriguing platform for data scientists with investigative and operational analytics applications. Apache Spark expands the possibilities for extensive data analysis and makes it easier for businesses to handle various significant data issues.

Apache Spark has been steadily rising in the big data environment. Spark has piqued the interest of data scientists due to its ability to keep data in memory, which speeds up machine learning tasks compared to Hadoop MapReduce. With IBM’s recent statement that it will teach Apache Spark to over 1 million data engineers and data scientists, 2016 is unquestionably THE year to learn Spark and pursue a rewarding profession.

2) Master Apache Spark to Maximize Your Big Data Investments:

To use Hadoop, many organizations are investing in innovative computing clusters. However, because enterprises may utilize Apache Spark on top of existing Hadoop clusters, there are no restrictions on investing in additional computing clusters.

Spark is compatible with Hadoop MapReduce, as well as YARN and HDFS. Because of Spark’s high compatibility with Hadoop, firms are on the verge of employing more Spark developers because they don’t have to re-invest in computing clusters because it works well with Hadoop. It gives professionals with Hadoop understanding a distinct advantage when it comes to studying Spark. You can enjoy this benefit by enrolling in a spark course. 

3) Acquire Apache Spark skills to keep up with the growing enterprise adoption rate:

The adoption rate of several adjacent big data technologies that complement Hadoop-Spark is expanding as enterprises embrace them. Spark is no longer only a component of Hadoop’s extensive data ecosystem; it’s also the go-to big data solution for businesses in various industries.

Compared to other open-source projects sponsored by the Apache Foundation, a recent poll on Spark adoption shows that the Spark community has received the most contributions. The use of a combination of Hadoop and Spark SQL to support BI workloads is becoming increasingly popular.

According to the survey results, 68% of firms that have adopted Apache Spark utilize BI workloads. Spark’s clear value proposition is causing organizations to adopt it faster, creating lucrative prospects for prominent data professionals who know Spark and Hadoop.

According to big data predictions for 2016, Apache Spark will go its own way, resulting in a unique, thriving ecosystem with popular cloud suppliers introducing their own Spark PaaS products. So join in a spark course immediately. 

4) Learn Apache Spark, as the demand for Spark developers is expected to rise in 2016:

Spark’s enterprise adoption is growing due to its ability to supersede Hadoop as the best alternative to MapReduce – both within and outside the Hadoop framework. Apache Spark, like Hadoop, demands technical competence in object-oriented programming ideas to program and run, which means that those with hands-on Spark experience will have more career chances. A Spark skills scarcity across the industry has resulted in several vacant employment and contracting opportunities for prominent data professionals.

Learning apache-spark opens many doors for folks who wish to work at the cutting edge of big data technology. There are various options for bridging the skills gap and landing a data-related career or a job as a Spark developer. The ideal choice is to enroll in a formal training program that provides hands-on experience and facilitates learning through projects.

5) To make a lot of money, learn Apache Spark:

Spark engineers are in such high demand that firms are willing to break recruitment rules, give attractive benefits, and allow flexible work hours solely to hire professionals in the Apache Spark programming language. According to Indeed.com, as of December 16, 2015, the average income for a Spark Developer in San Francisco was Rs 1,28,000. According to Indeed.com, the average pay for spark developers in San Francisco is 35% higher than the average salary for Spark developers in the United States.

Data engineers with experience with Apache Spark and Storm, according to O’Reilly, earn the highest average salary. According to many recent wage surveys, data engineers and data analysts with considerable data skills like Hadoop make close to Rs 1,20,000 per year, compared to the average IT expert salary of Rs 89,450/-. Compared to the total average compensation of data engineers, Rs 98,000, Apache Spark and Storm qualified experts to earn close to Rs 1,50,000 per year. People who want to advance their big data careers and earn high pay should start learning the Apache Spark course right now.

Now that you understand the benefits of apache-spark, you are waiting to enroll in a perfect course to build a prominent career. 

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