Apache Spark and Scala Training

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An open-source and robust framework, Apache Spark and Scala provides fast processing of Big Data, helping analysts provide result-oriented analytical results in an efficient manner. Apache Spark has built-in modules for streaming, takes lesser disk space and when integrated with Scala, offers high-end analytical power to the user to process Big Data.

Multisoft Systems offer high-end Apache Spark and Scala training in Noida for aspiring data analysts, looking to master Big Data. Apache Spark and Scala certification training provides comprehensive know-how and understanding of the Big Data Ecosystem.

After completing the Apache Spark and Scala training, the professionals will be able to work with following:

  • Learn Scala programing and its use in data analysis
  • Write Spark applications
  • 4-RDD Module understanding
  • Difference between Hadoop and Apache Spark and their relative use
  • Pattern matching with Scala
  • Scala Classes concepts
  • Spark streaming
  • Spark RDD and Scala Algorithms
Target audience

This course is ideal for candidates looking for working in Big Data using Apache Spark and Scala, Data Researchers, Analysts, and Software Programmers working with data analytics.


Aspirants opting for this course should have basic knowledge of Object Oriented Programming and functional programming.

1. Introduction to Spark

  • Limitations of MapReduce in Hadoop Objectives
  • Batch vs. Real-time analytics
  • Application of stream processing
  • How to install Spark
  • Spark vs. Hadoop Eco-system

2. Introduction to Programming in Scala

  • Features of Scala
  • Basic data types and literals used
  • List the operators and methods used in Scala
  • Concepts of Scala

3. Using RDD for Creating Applications in Spark

  • Features of RDDs
  • How to create RDDs
  • RDD operations and methods
  • How to run a Spark project with SBT
  • Explain RDD functions and describe how to write different codes in Scala

4. Running SQL queries Using SparkSQL

  • Explain the importance and features of SparkSQL
  • Describe methods to convert RDDs to DataFrames
  • Explain concepts of SparkSQL
  • Describe the concept of hive integration

5. Spark Streaming

  • Explain a concepts of Spark Streaming
  • Describe basic and advanced sources
  • Explain how stateful operations work
  • Explain window and join operations

6. Spark ML Programming

  • Explain the use cases and techniques of Machine Learning (ML)
  • Describe the key concepts of Spark ML
  • Explain the concept of an ML Dataset, and ML algorithm, model selection via cross validation

7. Spark GraphX Programming

  • Explain the key concepts of Spark GraphX programming
  • Limitations of the Graph Parallel system
  • Describe the operations with a graph
  • Graph system optimizations

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