Apache Spark and Scala Certification Training

Big Data
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Master Apache Spark and Scala skills with CertZip Apache Spark and Scala Certification Training and lead in your professional life.

Course Description

This Apache Spark training is created to help you master Apache Spark and the Spark Ecosystem, including Spark RDD, Spark SQL, and Spark MLlib. This Training is live, instructor-led & allows you to master key Apache Spark concepts with hands-on demonstrations.

Spark and Scala Certification Course Overview. This Spark certification training allows you to grasp the fundamental skills of the Apache Spark open-source framework and Scala programming language, having Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark.

Spark is primarily written in Scala. Most Spark tutorials and code samples are documented in Scala since it is the most prevalent language among Spark developers. Scala code is going to be type-safe, which has some benefits.

Apache Spark is an open-source foundation project. It enables us to perform in-memory analytics on large-scale data sets, and Spark can address some of the limitations of MapReduce.

High demand for Spark Developers in market It makes it easier to program and run. There is a massive opening of job opportunities for those who attain experience in Spark. If anyone wants to make their career in big data technology, they must learn apache spark. Only understanding Spark will extend a lot of opportunities.

Apache Spark's critical use case is its ability to process streaming data. With so much data being processed daily, it has become essential for companies to be able to stream and analyze it all in real-time. And Spark Streaming can handle this extra workload.

Apache Spark alone is a potent tool, and it is in high demand in the job market. If integrated with other means of Big Data, it makes a strong portfolio.

Apache Spark is a data processing framework that can quickly perform processing tasks on substantial data sets and distribute data processing tasks across multiple computers, either on its own or in tandem with other distributed computing tools.

What you'll learn

  • The CertZip Spark Training is designed to help you become a successful Spark developer. During this course, our expert instructors will train you to- Write Scala Programs to build Spark Application Master the concepts of HDFS Understand Hadoop 2.x Architecture Understand Spark and its Ecosystem Implement Spark operations on Spark Shell Implement Spark applications on YARN (Hadoop) Write Spark Applications using Spark RDD concepts Learn data ingestion using Sqoop Perform SQL queries using Spark SQL Implement various machine learning algorithms in Spark MLlib API and Clustering Explain Kafka and its components Understand Flume and its components Integrate Kafka with real time streaming systems like Flume Use Kafka to produce and consume messages Build Spark Streaming Application Process Multiple Batches in Spark Streaming Implement different streaming data sources


  • There are no such prerequisites for our Spark Scala Certification Training. However, prior knowledge of Java Programming and SQL will be helpful but is not at all mandatory.


Understand Big Data and its components such as HDFS. In this Apache Spark training module, you will learn about the Hadoop Cluster Architecture, Introduction to Spark and the difference between batch processing and real-time processing.

What is Big Data?
Big Data Customer Scenarios
Restrictions and Resolutions of present Data Analytics Architecture with Uber Use Case
How Does Hadoop Solves the Big Data Problem?
Hadoop’s Key Characteristics
Hadoop Ecosystem and HDFS
Hadoop Core Components
Rack Awareness and Block Replication
YARN and its Advantage
Hadoop Cluster and its Architecture
Hadoop: Different Cluster Modes
Hadoop Terminal Commands Preview
Big Data Analytics with Batch & Real-time Processing
Why is Spark needed?
What is Spark?
How does Spark differ from other frameworks?
Spark at Yahoo!
What is Hadoop? Preview

Learn the basics of Scala that are required for programming Spark applications. In this Apache Spark course module, you will also learn about the basic constructs of Scala such as variable types, control structures, collections such as Array, ArrayBuffer, Map, Lists, and many more.

What is Scala? Preview
Why Scala for Spark?
Scala in other Frameworks
Introduction to Scala REPL
Basic Scala Operations
Variable Types in Scala
Control Structures in Scala Preview
Foreach loop, Functions and Procedures
Collections in Scala- Array
ArrayBuffer, Map, Tuples, Lists, and more

In this Scala course module, you will learn about object-oriented programming and functional programming techniques in Scala.

Functional Programming
Higher Order Functions
Anonymous Functions
Getters and Setters
Custom Getters and Setters
Properties with only Getters
Auxiliary Constructor and Primary Constructor
Extending a Class Preview
Overriding Methods
Traits as Interfaces and Layered Traits

Understand Apache Spark and learn how to develop Spark applications. At the end, you will learn how to perform data ingestion using Sqoop.

Spark’s Place in Hadoop Ecosystem
Spark Components & its Architecture Preview
Spark Deployment Modes
Introduction to Spark Shell
Writing your first Spark Job Using SBT
Submitting Spark Job
Spark Web UI
Data Ingestion using Sqoop Preview

Get an insight of Spark - RDDs and other RDD related manipulations for implementing business logics (Transformations, Actions, and Functions performed on RDD).

Challenges in Existing Computing Methods
Possible Solution & How RDD cracks the Problem
What is RDD, It’s Operations, Transformations & Actions Preview
Data Loading and Saving Through RDDs Preview
Key-Value Pair RDDs
Other Pair RDDs, Two Pair RDDs
RDD Lineage
RDD Persistence
WordCount Program Using RDD Concepts
RDD Partitioning & How It Helps Achieve Parallelization
Passing Functions to Spark

In this Apache Spark online training module, you will learn about SparkSQL which is used to process structured data with SQL queries, data-frames and datasets in Spark SQL along with different kind of SQL operations performed on the data-frames. You will also learn about Spark and Hive integration. Topics:

Need for Spark SQL
What is Spark SQL? Preview
Spark SQL Architecture
SQL Context in Spark SQL
User Defined Functions
Data Frames & Datasets Preview
Interoperating with RDDs
JSON and Parquet File Formats
Loading Data through Different Sources
Spark – Hive Integration

Learn why machine learning is needed, different Machine Learning techniques/algorithms, and SparK MLlib.

Need for Kafka
What is Kafka? Preview
Core Concepts of Kafka
Kafka Architecture
Where is Kafka Used?
Understanding the Components of the Kafka Cluster
Configuring Kafka Cluster
Kafka Producer and Consumer Java API
Need for Apache Flume
What is Apache Flume? Preview
Basic Flume Architecture
Flume Sources
Flume Sinks
Flume Channels
Flume Configuration Preview
Integrating Apache Flume and Apache Kafka

Implement various algorithms supported by MLlib such as Linear Regression, Decision Tree, Random Forest and many more.

Drawbacks in Existing Computing Methods
Why is Streaming Necessary?
What is Spark Streaming? Preview
Spark Streaming Features
Spark Streaming Workflow Preview
How Uber Uses Streaming Data
Streaming Context & DStreams
Transformations on DStreams
Describe Windowed Operators and their Usefulness
Important Windowed Operators
Slice, Window, and ReduceByWindow Operators
Stateful Operators

Learning Objectives: In this Apache Spark and Scala training module, you will learn about the different streaming data sources such as Kafka and flume. At the end of the module, you will be able to create a spark streaming application.

Apache Spark Streaming: Data Sources
Streaming Data Source Overview Preview
Apache Flume and Apache Kafka Data Source
Example: Using a Kafka Direct Data Source
Perform Twitter Sentimental Analysis Using Spark Streaming


Spark can be 100x more speedy than Hadoop for large-scale data processing by exploiting in-memory computing and other optimizations. Ease of Use. Spark has easy-to-use APIs for working on large datasets. A Unified Engine.

Apache Spark is written in Scala. Many data engineers adopting Spark also adopt Scala, while Python and R remain prevalent with data scientists. Fortunately, you don't need to master Scala to use Spark effectively.

To better understand Apache Spark and Scala, one must learn as per the curriculum.

An Apache Spark developer's responsibilities include making Spark/Scala jobs for data aggregation and transformation, creating unit tests for Spark helper and transformations methods, using all code writing Scaladoc-style documentation, designing data processing pipelines.

Big Data framing and analysis · Programming languages: Python, Scala, Java · Spark SQL · Spark Streaming.

Understanding Spark is not challenging if you have a basic knowledge of Python or any programming language, as Spark delivers APIs in Java, Python, and Scala.

CertZip Support Team is for a lifetime and will be open 24/7 to help with your questions during and after completing the Apache Spark and Scala Certification Training.

By enrolling in the Apache Spark and Scala Training and completing the Module, you can get the CertZip Apache Kafka Training Certification.

$427 $449
$22 Off

Training Course Features


Every certification training session is followed by a quiz to assess your course learning.

Mock Tests
Mock Tests

The Mock Tests Are Arranged To Help You Prepare For The Certification Examination.

Lifetime Access
Lifetime Access

A lifetime access to LMS is provided where presentations, quizzes, installation guides & class recordings are available.

24x7 Expert Support
24x7 Expert Support

A 24x7 online support team is available to resolve all your technical queries, through a ticket-based tracking system.


For our learners, we have a community forum that further facilitates learning through peer interaction and knowledge sharing.


Successfully complete your final course project and CertZip will provide you with a completion certification.

Apache Spark and Scala Certification Training

Apache Spark and Scala Training demonstrate that the holder has the proficiency and aptitudes needed to work with Apache Kafka.

This Training would help you clear the CCA Spark and Hadoop Developer (CCA175) Examination. You will understand the basics of Big Data and Hadoop, and you will learn how Spark enables in-memory data processing and runs much faster than Hadoop MapReduce.

If you're ready for a career in a stable and high-paying field, Apache Spark and Scala might be right for you, and this Certification is the place to start.

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