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Apache Spark and Scala Training

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. Binary Talks 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.

Course Duration :- 40 hours
Upon the completion of the Informatica course, the candidates will be able to do the 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


Accredited Training Partner

To teach real programming skills

Build a solid understanding

Educated Staff


Video Lessons

Modules / Levels

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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