6 Important Reasons To Learn Apache Spark?


1. Objective

With the increasing size of data getting generated every second, it has become important to analyze this data to get important business insights in a lesser time. Several Big data options like HadoopStormSpark, Flink etc. have made this possible. But out of hundreds of choices available, why should you learn Apache Spark, how Apache Spark has replaced Hadoop and became most popular Big Data Engine and why is industry running behind Spark is a major concern. So let’s see the reasons to learn Apache Spark.

6 Most Important Reasons to Learn Apache Spark.

2. Top 6 Reasons to Learn Apache Spark?

Let’s now see the reason for why should you learn Apache Spark?

Apache Spark is a next Gen Big Data Tool. It provides both batch and streaming processing capabilities for faster data processing. 9 Out of 10 Companies have started using Apache Spark for their data processing. Because of its wide range of applications and ease of use to work with, Spark is also called the Swiss army knife of Big Data Analytics. To learn more about Apache Spark follow this comprehensive guide. Here are few reasons to learn Apache Spark now and keep yourself moving technically ahead of others:

2.1. High compatibility with Hadoop

When Hadoop came into the picture, companies started investing in this technology. Even professionals from varied domains started learning it quickly. By the time Apache Spark was launched companies have already invested hugely in Hadoop (especially hardware and resources), it is not feasible to invest again for Spark.

Hence, Spark has come up with compatibility with Hadoop: Spark can be deployed on the same hardware of Hadoop and can use its resource management layer – Yarn apart from this Spark can process the data stored in HDFS (Hadoop Distributed File System). If you are a professional with Hadoop knowledge, learning Spark would be advantageous as companies are now looking for Spark experts rather than Hadoop alone.

2.2. Hadoop is dwindling while Spark is sparking

Spark is 100 times faster than MapReduce. It is easier to program in Spark as compared to Map Reduce. This has made Spark one of the top Apache Projects. Apache Spark is an in-memory data processing framework with all Hadoop capabilities. With the coming of Apache Spark, it has been projected for the possibility of the end of Map Reduce era. Hadoop was limited to just MapReduce, but Spark is a generalized framework to process the huge volume of data. Learn more about the differences between Hadoop vs Spark.

2.3. Increased access to Big Data

In today’s leading world we are generating multi-terabytes of data. The amount of data generation is increasing day by day and this huge volume of data can’t be accessed by traditional methods. So for eliminating the problem of Big Data, Hadoop emerged, but it has some limitations which are eliminated by Apache Spark.

Spark is more efficient than Hadoop due to its real time processing. Most of the data scientist prefer to work with Spark as it less complex and because of its fast speed. Data scientist prefer works with Spark mainly because of as Spark has the ability to store data resident in memory that helps speed up machine learning workloads.

2.4. High demand for Spark professionals

Spark is fastly becoming an ecosystem itself. Spark toolset is also constantly expanding which is attracting growing third-party interest. According to John Trippier, Alliances and Ecosystem Lead at Databricks, “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”. So you can give a boost to your career and salary by learning Apache Spark.

2.5. Diverse

Spark enables users to write applications in Java, Scala, Python, R. This helps them to create and run their applications on programming languages they are comfortable with. Spark also scores on many senses. You can write a custom Spark big data app, use Spark SQL and do data analysis using SQL, set up ETL pipelines using Spark, use Spark streaming and make it part of real-time data pipeline, use MLlib machine learning library and run Analytics… Or even do graph processing with GraphX. On top of this, Scala is supported by Java which helps write concise code. It can replace a 50 line java map-reduce code with a 2-3 line Scala Spark code.

2.6. Apache Spark to make Big Money

Spark is the tool with the greatest coefficient. According to indeed.com, the average salary for a Spark Developer in San Francisco is $128,000 as of December 16, 2015.

According to the O’Reilly 2014 Data Science Salary Survey, Spark developers earn the highest median salary among developers using the 10 most widely used Big Data development tools. In 2015 Data Science Salary Survey, O’Reilly found strong correlations between those who used Apache Spark and Scala and those who were paid more money.

In one of its models, using Spark added more than $11,000 to the median salary, while Scala had about a $4,000 impact to the bottom line. O’Reilly says in its report. According to it, “learning Spark could apparently have more impact on salary than getting a Ph.D. Scala is another bonus: those who use both are expected to earn over $15,000 more than an otherwise equivalent data professional.

So what are you waiting for then when Apache Spark has so many additional features for you. Learn now and grab the opportunity in the market.

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