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

How to become an Apache Spark Developer?

Last updated on Apr 22,2020 1.1K Views
Ravi Kiran
Tech Enthusiast working as a Research Analyst at Edureka. Curious about learning... Tech Enthusiast working as a Research Analyst at Edureka. Curious about learning more about Data Science and Big-Data Hadoop.

Apache Spark is the most powerful, flexible, and a standard for in-memory data computation capable enough to perform Batch-Mode, Real-time and Analytics on the Hadoop Platform. This integrated part of Cloudera is the highest-paid and trending technology in the current IT market.

Today, in this article, we will discuss how to become a successful Spark Developer through the docket below.

 

What makes Spark so powerful?

Apache spark is the multi-role jet fighter in combat with the colossal loads of Big-Data in Data Analytics. It is capable to deal with almost all types of data irrelevant to its structure and size with lightning speeds. Here are a few reasons for which the Spark is considered the most powerful Big Data tool.

  • Integration with Hadoop

Spark can be directly integrated on to Hadoop’s HDFS and work as an excellent Data Processing Tool. Coupled with YARN, It can run on the same cluster along the side of MapReduce Jobs.

  • Meet the global Standards

Learning spark has become one of the global standards as there is an impeccable raise in the world of Big Data Analytics with Apache Spark standing by its side.

  • Faster than MapReduce

There is a lot of performance gap when it comes to deciding between MapReduce and Spark. The lightning-fast performance due to its In-Memory Processing Capability brought Spark its place amongst the top-level Apache Projects.

  • Capable to perform in Production Environment

The simple and faster programming interface of spark can support top-notch programming languages like Scala, Java and Python. This gave a staggering edge to Spark to be the leading legend in the Production Environment with a massive surge in its demand.

  • Raising Demand for Spark Developers

Due to its Outperformed Capabilities and reliability, Spark is preferred by many Top MNCs like Adobe, Yahoo, NASA and many more. Proportionately, the demand for Spark Developers is also visualizing a rapid rise.

To know more about the Spark importance in the current IT market, please go through this article

 

Introduction to Apache Spark

spark-machine learning framework-edureka

Apache Spark is the Open-Source software utility by the Apache Foundation. It was designed and deployed as an upgrade to Apache Hadoop’s processing capabilities. unlike the common myth, Apache Spark is never the replacement to Hadoop. It is another processing layer like MapReduce.

Now, the definition. Apache Spark is the lightning-fast cluster computing framework that provides the interface to program the entire cluster so as to achieve implicit data parallelism and fault-tolerance.

 

Road map to become Apache Spark Developer

There is a thin line of the gap between actually becoming a certified Apache Spark Developer and to be an actual Apache Spark Developer capable enough perform in the real-time application.

  • So, to become an expert level Spark Developer you need to follow the right path the expert level guidance from the certified real-time industry experts in the industry. For a beginner, it is the best time to take up a training and certification program.
  • Once the Certification has begun, you should start up with your own projects to understand the working terminology of Apache Spark. Spark’s major building blocks are the RDDs(Resilient Distributed Datasets) and the Dataframes.
  • Spark also has the capability to get itself integrated with some of the high-performance programming languages like Python, Scala and Java. PySpark RDDs are the best examples for the combination of Python and Apache Spark.
  • You can also understand how to integrate Java with Apache Spark through this amazing Spark Java Tutorial article.
  • Once you are having a better grip on the major building block of Spark, you can move ahead into learning the Major Components in Apache Spark which are mentioned below:

and a lot more

  • Once you get the required training and certification, its time for you to take the most important and bigger leap. The CCA-175 Certification. You can begin solving some sample CCA-175 Hadoop and Spark Certification Examination.
  • Once you get a briefer idea and confidence, you could register for CCA-175 Examination and excel with your true Spark and Hadoop Developer Certification. You can refer to a sample CCA-175 Question paper here.

 

Apache Spark Developer Salary

Apache Spark Developers is one of the most highly decorated professionals with handsome salary packages compared to others. We will now discuss the salary trends of Apache Spark Developers in different nations.

First, India.

In India, the average salary offered to an entry-level Spark Developer is in between 600,000 to 1,000,000₹ per annum. On the other hand, for an experienced level Spark Developer, the salary trends are in between 2,500,000 to 4,000,000₹ per annum.

Next, in the United States of America, the salary offered for a beginner level Spark Developer is 75,000$ to 100,000$ per annum. Similarly, for an experienced level Spark developer, the salary trends are in between 145,000$ to 175,000$ per annum.

Now, let us understand the skills, roles and responsibilities of an Apache Spark Developer.

 

Apache Spark Developer Skills

Spark developer skills required edureka

  • Load data from different data platforms into the Hadoop platform using ETL tools.
  • Decide an effective file format for a specific task.
  • Based on business requirements, clean data through streaming API or user-defined functions.
  • Effectively schedule Hadoop jobs.
  • Hand holding with Hive and HBase for schema operations.
  • Capability to work on Hive tables to assign schemas.
  • Deploy HBase clusters and continuously manage them.
  • Execute pig and hive scripts to perform various joins on datasets
  • Applying different HDFS formats and structure like to speed up analytics. 
  • Maintaining the privacy and security of Hadoop clusters.
  • Fine-tuning of Hadoop applications.
  • Troubleshooting and debugging any Hadoop ecosystem at runtime.
  • Installing, configuring and maintaining enterprise Hadoop environment if required

 

Apache Spark Developer Roles and Responsibilities

roles and responsibilities of a spark developer

  • Capable to write executable code for Analytics, Services and Spark Components.
  • Knowledge in high-performance programming languages like Java, Python, and Scala.
  • Should be well-versed with related technologies like Apache Kafka, Storm, Hadoop, and Zookeeper.
  • Ready to be responsible for System Analysis that includes Design, Coding, Unit Testing and other SDLC activities.
  • Gathering user requirements and converting them to strong technical tasks and provide economical estimates for the same.
  • Should be a team-player with global standards so as to understand project delivery risks.
  • Ensure the quality of technical analysis and expertise in solving issues.
  • Review code, use-case and ensure it meets the requirements.

 

Companies using Apache Spark

Apache Spark is one of the widest spread technology that changed the faces of many IT industries and helped them to achieve their current accomplishments and further. Let us now discuss some of the Tech Giants and Major Players in the IT industry that are in the use of Spark.

Hadoop Developer Skills Companies Hiring Hadoop Developers

So, with this, we come to an end of this “How to become a Spark Developer?” article. I hope we sparked a little light upon your knowledge about Spark, Scala and Hadoop along with CCA-175 certification features and its importance.

This article based on Apache Spark and Scala Certification Training are designed to prepare you for the Cloudera Hadoop and Spark Developer Certification Exam (CCA175). You will get in-depth knowledge on Apache Spark and the Spark Ecosystem, which includes Spark RDD, Spark SQL, Spark MLlib and Spark Streaming. You will get comprehensive knowledge on Scala Programming language, HDFS, Sqoop, Flume, Spark GraphX and Messaging System such as Kafka.

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