Why You Should Learn Apache Spark with Scala

When it comes to working on any Apache Spark project, Big Data developers can choose from a variety of programming languages that are supported by Apache Spark. This includes languages like Java, Python, R, and of course, the Scala language that was used to develop Spark. The Scala programming language has now become the language of choice among developers working on Big data frameworks like Apache Spark and Kafka.

A market survey conducted by Databricks reveals that 71% of the Apache Spark users are using the Scala programming language. Another industry survey on Apache Spark by Typesafe reveals that 88% of the Spark users were using Scala followed by 44% (for Java) and 22% (for Python).

Why use Scala for Apache Spark Programming

Short for Scalable Language, Scala is a general-purpose and open-source programming language designed to write applications in a concise and type-safe mode. As an object-oriented language, Scala is flexible enough to interact with Java code and can even include Java code inside a Scala class.

Why is Scala preferred for programming in Apache Spark? Here are the main reasons:

  • Apache Spark was written using Scala.

This allows Scala developers to access and understand the Spark source code thus allowing them to implement new features within Spark.

  • Less complex than C++ or Java

As compared to other programming languages like Python, R, C++, or Java, Scala uses basic coding syntax and lambda making this language easier to learn and master for new developers. Additionally, data analysis using Scala shows higher performance as compared to data analytics tools using Python or R.

  • Suited for Big data applications

Thanks to Scala’s support for concurrency and libraries, Scala language is more suited for developers building scalable Big data applications.

  • Support for functional programming

Scala-based development enables functional programming thanks to its support for data structures, comprehensions, and immutable values.

How an Apache Spark training with Scala can boost your career prospects

Leading innovation companies such as LinkedIn, Twitter, Netflix, and Tumblr are using Scala for their programming requirements. The global market for Apache Spark is set to grow at a healthy CAGR rate of 67% between 2019 and 2022.

All these industry trends are creating a huge demand for professionals skilled in Apache Spark and Scala. Professionals attending a certified Apache Spark and Scala training program from a premier institute are expected to be hired at a starting salary of $100,000.

Apart from the huge career benefits, an Apache Spark training course can help you master key Apache Spark and Scala concepts such as Core Spark, Spark Internals, GraphX, and Spark MLlib. On completing this Apache Spark with Scala certification course, you are prepared to appear for the Spark and Hadoop Developer Certification examination that is conducted by Cloudera Certified Associate (or CCA).

Attending students can also benefit from the hands-on experience they get while working on Apache Spark and Scala projects during the certification program.

Prerequisites for the Apache Spark and Scala training

The Apache Spark and Scala training program does not have any mandatory prerequisites but students familiar with Java or Python programming can benefit the most from this course. Other than this, students with working knowledge of SQL database language or Linux systems are eligible for this training program.

Brief Outline of the Apache Spark and Scala Training Course

With around 24 hours (or 3 days) of intensive instructor-led training, the Apache Spark and Scala training program can help students master the practical concepts of the Apache Spark framework. Along with this, they can master the Scala programming language and its application in Spark projects.

At the end of this professional training course, students can master the following concepts:

  • Big Data and Hadoop Cluster architecture
  • Basics of Scala programming
  • Apache Spark framework and methodologies
  • Apache Spark data structures
  • Apache Spark ecosystem comprising of SparkSQL, Spark Context, Apache Spark Streaming, MLlib, and Spark GraphX
  • Kafka architecture and Kafka cluster concepts

Along with learning basic concepts through classroom sessions, students attending the Apache Spark training can learn from practical exercises and industry case studies presented by a certified instructor with extensive industry experience in Apache Spark skills. Students also get the opportunity to work on industry projects based on Apache Spark framework.

Aspiring for a rewarding and lucrative career in the field of Big data analytics? Then this Apache Spark and Scala training course is just what you need to make an ideal start for your career aspirations.


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