Big Data Analytics with Spark Training

Master Big Data with Spark: Scala, RDDs, Spark SQL, MLlib, and Streaming

Platform:

Online

In-class

Revised and Updated: 28 September 2026

Date	Venue	Duration
07 - 11 December 2026	Sandton, Gauteng	5 Days
15 - 19 March 2027	Sandton, Gauteng	5 Days

Course Introduction

The analysis of large datasets involves using an equally large set of computers. Successfully using so many computers entails the use of distributed file systems, such as the Hadoop Distributed File System (HDFS), and parallel computational models, such as Hadoop, MapReduce, and Spark.

In this Big Data Analytics with Spark Training course, you will learn the essential components of large-scale parallel computation projects and how to use Spark to minimise bottlenecks. This course will teach you how to conduct supervised and unsupervised machine learning on substantial datasets using the Machine Learning Library (MLlib) and gain hands-on experience using PySpark.

Skills Covered

This Big Data Analytics with Spark Training programme will provide you with knowledge and expertise in:

Scala programming
Spark installation
Resilient Distributed Datasets (RDD)
Spark SQL
Spark Streaming
Spark ML programming
GraphX programming

Course Objectives

Upon successfully completing this Big Data Analytics with Spark Training course, participants will be able to:

Obtain an overview of Big Data and Hadoop, including HDFS and YARN (Yet Another Resource Negotiator)
Gain comprehensive knowledge of various tools in the Spark ecosystem
Understand how to ingest data into HDFS using Sqoop and Flume
Program Spark using PySpark
Identify the computational trade-offs in a Spark application
Model data using statistical and machine learning methods
Use real-time data feeds through a publish-subscribe messaging system like Kafka
Gain exposure to various real-life, industry-based projects
Study projects in diverse domains, such as banking, telecommunications, social media, and government

Organisational Benefits

Companies that send employees to participate in this Big Data Analytics with Spark Training course can benefit by:

Adopting technology used successfully by multiple companies in various domains globally
Positioning their organisation to capitalise on continued industry-wide growth in big data investment
Providing the workforce with flexible and cost-effective professional development opportunities
Analysing case studies in this domain and applying successful techniques in their organisation
Comprehending the principles and practice of Big Data Analytics and its operational context

Who should attend?

Developers and Architects
BI / ETL / DW Professionals
Senior IT Professionals
Testing Professionals
Mainframe Professionals
Recent Graduates
Big Data Enthusiasts
Software Architects, Engineers, and Developers
Data Scientists and Analytics Professionals

Training Methodology

Our diverse instructional approaches ensure effective learning:

– Lectures & Presentations: Engage with expert-driven, stimulating content.
– Course Material: Access well-crafted supporting resources.
– Group Work: Collaborate on discussions and case studies for practical insights.
– Workshops & Role-Play: Participate in immersive, scenario-based activities.
– Practical Application: Focus on applying theoretical knowledge in real situations.
– Post-Training Support: Receive extensive support after training for skill implementation.

Training Outline

5-Day Comprehensive — Training Outline

Day 1 — Introduction to Big Data, Hadoop, and Scala

Module 1: Introduction to Big Data, Hadoop, and Spark

What is Big Data? Big Data customer scenarios
Big Data and Hadoop; how Hadoop solves the Big Data problem
Hadoop's key characteristics, ecosystem, and HDFS
Hadoop core components; rack awareness and block replication
YARN and its advantages
Hadoop cluster architecture and different cluster modes
Why Spark is needed; what is Spark and how it differs from other frameworks

Module 2: Introduction to Scala for Apache Spark

What is Scala? Why Scala for Spark?
Control structures in Scala
Foreach loop, functions, and procedures
Collections in Scala: Array, ArrayBuffer, Map, Tuples, Lists, and more
Introduction to the Scala REPL and basic Scala operations
Variable types in Scala
Practical Exercise: Demo: Working through the Scala REPL in detail.

Day 2 — Scala Programming and the Spark Framework

Module 3: Functional Programming and OOP Concepts in Scala

Auxiliary constructor and primary constructor
Singletons; extending a class; overriding methods
Traits as interfaces and layered traits
OOP concepts and functional programming
Higher-order functions and anonymous functions
Classes in Scala; getters and setters

Module 4: Deep Dive into the Apache Spark Framework

Submitting a Spark job; the Spark Web UI
Data ingestion using Sqoop
Building and running a Spark application
Spark's place in the Hadoop ecosystem
Spark components and architecture; deployment modes
Introduction to the Spark shell
Writing your first Spark job using SBT
Configuring Spark properties

Day 3 — Spark RDDs, DataFrames, and Spark SQL

Module 5: Playing with Spark RDDs

Challenges in existing computing methods and how RDDs solve the problem
What is an RDD? Operations, transformations, and actions
RDD persistence and lineage
Loading and saving data through RDDs
Key-value pair RDDs and other pair RDDs
RDD partitions
Passing functions to Spark
Practical Exercise: Lab: WordCount program using RDD concepts.

Module 6: DataFrames and Spark SQL

The need for Spark SQL; what is Spark SQL?
Spark SQL architecture
Spark-Hive integration
Creating DataFrames; loading and transforming data through different sources
SQL Context in Spark SQL
User-defined functions
DataFrames and Datasets; interoperating with RDDs
JSON and Parquet file formats
Practical Exercise: Lab: Stock market analysis using Spark SQL.

Day 4 — Machine Learning with Spark MLlib

Module 7: Machine Learning Using Spark MLlib

Why and what is Machine Learning; where it is used
Different types of Machine Learning techniques
Introduction to MLlib; features and tools
Various ML algorithms supported by MLlib
Practical Exercise: Use Case: Face detection with Machine Learning.

Module 8: Deep Dive into Spark MLlib

K-Means clustering
Linear regression
Logistic regression
Decision tree
Random forest

Day 5 — Kafka, Flume, Spark Streaming, and GraphX

Module 9: Understanding Apache Kafka and Apache Flume

What is Apache Flume and why is it needed?
Basic Flume architecture: sources, sinks, and channels
Flume configuration
What is Kafka? Core concepts and architecture
Where is Kafka used; understanding the Kafka cluster
Configuring Kafka clusters; producing and consuming messages
Integrating Apache Flume and Apache Kafka
Practical Exercise: Lab: Streaming Twitter data into HDFS.

Module 10: Spark Streaming — Multiple Batches

Why streaming is necessary; drawbacks in existing computing methods
What is Spark Streaming? Features and workflow
Streaming context and DStreams
Transformations on DStreams
Windowed operators: slice, window, and reduceByWindow
Stateful operators

Module 11: Apache Spark Streaming — Data Sources

Apache Spark Streaming data sources overview
Apache Flume and Apache Kafka data sources
Using a Kafka direct data source
Practical Exercise: Lab: Twitter sentiment analysis using Spark Streaming.

Module 12: Spark GraphX

Key concepts of Spark GraphX
GraphX algorithms and their implementations

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FAQs – Big Data Analytics with Spark Training

Explore Big Data Analytics with Spark, covering Apache Spark, data processing, distributed computing, data analysis, Python, machine learning, and scalable analytics.

What is covered in the Big Data Analytics with Spark Training?

The course covers Big Data and Hadoop, Scala programming, Apache Spark, RDDs, DataFrames, Spark SQL, Spark MLlib, Spark Streaming, Kafka, Flume, and GraphX.

Who should attend Big Data Analytics with Spark Training?

It is suitable for developers, software architects, engineers, BI and ETL professionals, senior IT professionals, data scientists, analytics professionals, testing professionals, recent graduates, and Big Data enthusiasts.

Does the course cover Scala programming and Apache Spark?

Yes. Participants learn Scala fundamentals, functional and object-oriented programming, Spark architecture, Spark applications, deployment modes, Spark jobs, and Spark configuration.

Does the Big Data Analytics with Spark course cover RDDs, DataFrames and Spark SQL?

Yes. Training covers RDD operations, transformations, actions, persistence, partitions, DataFrames, Datasets, Spark SQL, Hive integration, JSON and Parquet formats, and data transformation.

Does the training cover machine learning and real-time data streaming?

Yes. Participants explore Spark MLlib algorithms such as K-Means, linear and logistic regression, decision trees and random forests, together with Kafka, Flume and Spark Streaming for real-time data.

Does the Big Data Analytics with Spark Training include practical exercises?

Yes. The five-day training includes practical labs and industry-based scenarios covering Scala, RDDs, Spark SQL, machine learning, Kafka, Spark Streaming, sentiment analysis, and other Big Data applications.

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Big Data Analytics with Spark Training

Master Big Data with Spark: Scala, RDDs, Spark SQL, MLlib, and Streaming

Course Objectives

Who should attend?

Course Outline

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Course Introduction

Skills Covered

Course Objectives

Organisational Benefits

Who should attend?

Training Methodology

Training Outline

5-Day Comprehensive — Training Outline

Course Categories

Course Registration Guide

Request a Call Back

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Success Stories

SQL Server Training

Basic Registry, Records and Archives

Document Control and Document Management Systems

Data & Information Governance

FAQs – Big Data Analytics with Spark Training

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