Apache Spark Certification Training

Apache Spark Certification Training


Trainerkart’s Apache Spark and Scala Certification Training is designed to provide you the knowledge and skills that are required to become a successful Spark Developer and prepare you for the Cloudera Hadoop and Spark Developer Certification Exam (CCA175). Throughout the Apache Spark Training, you will get an in-depth knowledge on Apache Spark and the Spark Ecosystem, which includes Spark RDD, Spark SQL, Spark MLlib and Spark Streaming.You will also get comprehensive knowledge on Scala Programming language, HDFS, Sqoop, FLume, Spark GraphX and Messaging System such as Kafka.

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Apache Spark Certification Training

Apache Spark Certification Training will enable learners to understand how Spark executes in-memory data processing and runs much faster than Hadoop MapReduce. Learners will master Scala programming and will get trained on different APIs which Spark offers such as Spark Streaming, Spark SQL, Spark RDD, Spark MLlib and Spark GraphX. This Trainerkart course is an integral part of Big Data developer’s learning path.

Course objectives

After completing the Apache Spark training, you will be able to:

1) Understand Scala and its implementation

2) Master the concepts of Traits and OOPS in Scala programming

3) Install Spark and implement Spark operations on Spark Shell

4) Understand the role of Spark RDD

5) Implement Spark applications on YARN (Hadoop)

6) Learn Spark Streaming API

7) Implement machine learning algorithms in Spark MLlib API

8) Analyze Hive and Spark SQL architecture

9) Understand Spark GraphX API and implement graph algorithms

10) Implement Broadcast variable and Accumulators for performance tuning

11) Project

Why learn Apache Spark?

In this era of ever-growing data, the need for analyzing it for meaningful business insights is paramount. There are different big data processing alternatives like Hadoop, Spark, Storm and many more. Spark, however, is unique in providing batch as well as streaming capabilities, thus making it a preferred choice for lightning fast big data analysis platforms.

Apache Spark is the new market buzz and having Big Data and Hadoop &  Apache Kafka skills is a highly preferred learning path after the Apache Spark & Scala training.

Who should go for this course?

This course is a must for anyone who aspires to embark into the field of big data and keep abreast of the latest developments around fast and efficient processing of ever-growing data using Spark and related projects. The course is ideal for:

1. Big Data enthusiasts

2. Software Architects, Engineers and Developers

3. Data Scientists and Analytics professionals

What are the pre-requisites for this Apache Spark Certification Training course?

A basic understanding of functional programming and object oriented programming will help. Knowledge of Scala will definitely be a plus, but is not mandatory.


What are the system requirements for this course?

The requirement for this course is a system with Intel i3 processor or above and minimum 4GB RAM.

How will I execute the Practicals?

For your practical work, we will help you set up a Virtual Machine on your system with IDE for Scala. This will be local access for you. The detailed step-wise installation guides are present in your LMS which will help you to install and set-up the environment for Spark and Scala. In case you come across any doubt, the 24*7 support team will promptly assist you.

Which Case-Studies will be part of the Apache Spark Certification Training course?

Project #1: Design a system to replay the real time replay of transactions in HDFS using Spark.
Technologies Used: 
1. Spark Streaming
2. Kafka (for messaging)
3. HDFS (for storage)
4. Core Spark API (for aggregation)
Project #2: Drop-page of signal during Roaming
Roaming Industry: Telecom Industry
Problem Statement: You will be given a CDR (Call Details Record) file, you need to find out top 10 customers facing frequent call drops in Roaming. This is a very important report which telecom companies use to prevent customer churn out, by calling them back and at the same time contacting their roaming partners to improve the connectivity issues in specific areas.

We also provide training in many other courses under Big Data And Analysis.

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Apache Spark Certification Training

100% Money Back Guarantee*

Instructor-led Sessions

24 Hours of Online Live Instructor-Led Classes.
Weekend Class : 8 sessions of 3 hours each.
Weekday Class : 12 sessions of 2 hours each.

Real-life Case Studies

Towards the end of the course, you will be working on a Real Life project.


Each class will be followed by practical assignments which will aggregate to minimum 25 hours.

Lifetime Access

You get lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.

24 x 7 Expert Support

We have 24×7 online support team available to help you with any technical queries you may have during the course.


Towards the end of the course, you will be working on a project. Edureka certifies you as an Spark Expert based on the project.

Apache Spark Certification Training

Why this Apache Spark Certification Training?

  • Spark has overtaken Hadoop as the most active open source Big Data framework – Forbes
  • Apache Spark will dominate the Big Data landscape by 2022 – Wikibon
  • The average pay stands at 10 8, 366 USD p.a – Indeed.com

Apache Spark Certification Training

Trainerkart Certification Process:

  • Once you are successfully through the project (Reviewed by an expert), you will be awarded with Trainerkart’s Apache Spark Certificate.
  • Trainerkart,s certification has industry recognition and we are the preferred training partner for many MNCs e.g.Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mindtree, BNYMellon etc. Please be assured.

Apache Spark Certification Training

Introduction to Scala for Apache Spark

Learning Objectives – In this module, you will understand the basics of Scala that are required for programming Spark applications. You can learn about the basic constructs of Scala such as variable types, control structures, collections, and more.

Topics – What is Scala? Why Scala for Spark? Scala in other frameworks, introduction to Scala REPL, basic Scala operations, Variable Types in Scala, Control Structures in Scala, Foreach loop, Functions, Procedures, Collections in Scala- Array, ArrayBuffer, Map, Tuples, Lists, and more.

OOPS and Functional Programming in Scala

Learning Objectives – In this module, you will learn about object oriented programming and functional programming techniques in Scala.

Topics – Class in Scala, Getters and Setters, Custom Getters and Setters, Properties with only Getters, Auxiliary Constructor, Primary Constructor, Singletons, Companion Objects, Extending a Class, Overriding Methods, Traits as Interfaces, Layered Traits, Functional Programming, Higher Order Functions, Anonymous Functions, and more.

Introduction to Big Data and Apache Spark

Learning Objectives – In this module, you will understand what is big data, challenges associated with it and the different frameworks available. The module also includes a first-hand introduction to Spark.

Topics – Introduction to big data, challenges with big data, Batch Vs. Real Time big data analytics, Batch Analytics – Hadoop Ecosystem Overview, Real-time Analytics Options, Streaming Data – Spark, In-memory data – Spark, What is Spark?, Spark Ecosystem, modes of Spark, Spark installation demo, overview of Spark on a cluster, Spark Standalone cluster, Spark Web UI.

Spark Common Operations

Learning Objectives – In this module, you will learn how to invoke Spark Shell and use it for various common operations.

Topics – Invoking Spark Shell, creating the Spark Context, loading a file in Shell, performing basic Operations on files in Spark Shell, Overview of SBT, building a Spark project with SBT, running Spark project with SBT, local mode, Spark mode, caching overview, Distributed Persistence.

Playing with RDDs

Learning Objectives – In this module, you will learn one of the fundamental building blocks of Spark – RDDs and related manipulations for implementing business logics.

Topics – RDDs, transformations in RDD, actions in RDD, loading data in RDD, saving data through RDD, Key-Value Pair RDD, MapReduce and Pair RDD Operations, Spark and Hadoop Integration-HDFS, Spark and Hadoop Integration-Yarn, Handling Sequence Files, Partitioner.

Spark Streaming and MLlib

Learning Objectives – In this module, you will learn about the major APIs that Spark offers. You will get an opportunity to work on Spark streaming which makes it easy to build scalable fault-tolerant streaming applications, MLlib which is Spark’s machine learning library.

Topics – Spark Streaming Architecture, first Spark Streaming Program, transformations in Spark Streaming, fault tolerance in Spark Streaming, checkpointing, parallelism level, machine learning with Spark, data types, algorithms – statistics, classification and regression, clustering, collaborative filtering.

GraphX, SparkSQL and Performance Tuning in Spark

Learning Objectives – In this module, you will learn about Spark SQL that is used to process structured data with SQL queries, graph analysis with Spark, GraphX for graphs and graph-parallel computation. You will also0 get a chance to learn the various ways to optimize performance in Spark.

Topics – Analyze Hive and Spark SQL architecture, SQLContext in Spark SQL, working with DataFrames, implementing an example for Spark SQL, integrating hive and Spark SQL, support for JSON and Parquet File Formats, implement data visualization in Spark, loading of data, Hive queries through Spark, testing tips in Scala, performance tuning tips in Spark, shared variables: Broadcast Variables, Shared Variables: Accumulators.

A complete project on Apache Spark

Learning Objectives – In this module, you will get an opportunity to work on a live Spark project where you can implement the learnings from previous modules hands-on, and solve a real-time use case.

Problem Statement: Design a system to replay the real time replay of transactions in HDFS using Spark.

Technologies Used: 

1. Spark Streaming

2. Kafka (for messaging)

3. HDFS (for storage)

4. Core Spark API (for aggregation)


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