Our Apache Spark course offers comprehensive training in big data processing and analytics using a powerful open-source framework.
Participants will learn to harness the potential of Spark for distributed data processing and machine learning tasks.
Hands-on labs and projects are an integral part of the course, ensuring practical skill development.
We cover topics like Spark architecture, data manipulation, and optimizing performance for real-world applications.
Students gain insights into Spark's ecosystem, including Spark SQL, streaming, and MLlib.
By the end of the course, participants will be equipped to build scalable, high-performance data pipelines.
Enroll in our Apache Spark course to unlock the potential for data-driven insights and career opportunities.

Module 1: Introduction to Spark

Introduction to Spark
Spark overcomes the drawbacks of working on MapReduce
Understanding in-memory MapReduce
Interactive operations on MapReduce
Spark stack, fine vs. coarse-grained update, Spark Hadoop YARN, HDFS Revision, and YARN Revision
The overview of Spark and how it is better than Hadoop
Deploying Spark without Hadoop
Spark history server and Cloudera distribution

Module 2: Spark Basics

Module 3: Working with RDDs in Spark

Spark RDD
Creating RDDs
RDD partitioning
Operations and transformation in RDD
Deep dive into Spark RDDs
The RDD general operations
Read-only partitioned collection of records
Using the concept of RDD for faster and efficient data processing
RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions

Module 4: Aggregating Data with Pair RDDs

Module 5: Writing and Deploying Spark Applications

Comparing the Spark applications with Spark Shell
Creating a Spark application using Scala or Java
Deploying a Spark application
Scala built application
Creation of the mutable list, set and set operations, list, tuple, and concatenating list
Creating an application using SBT
Deploying an application using Maven
The web user interface of Spark application
A real-world example of Spark
Configuring of Spark

Module 6: Parallel Processing

Module 7: Spark RDD Persistence

The execution flow in Spark
Understanding the RDD persistence overview
Spark execution flow, and Spark terminology
Distribution shared memory vs RDD
RDD limitations
Spark shell arguments
Distributed persistence
RDD lineage
Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey

Module 8: Spark MLlib

Introduction to Machine Learning
Types of Machine Learning
Introduction to MLlib
Various ML algorithms supported by MLlib
Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques

Module 9: Integrating Apache Flume and Apache Kafka

Module 10: Spark Streaming

Introduction to Spark Streaming
Features of Spark Streaming
Spark Streaming workflow
Initializing StreamingContext, discretized Streams (DStreams), input DStreams and Receivers
Transformations on DStreams, output operations on DStreams, windowed operators and why it is useful
Important windowed operators and stateful operators

Module 11: Improving Spark Performance

Introduction to various variables in Spark like shared variables and broadcast variables
Learning about accumulators
The common performance issues
Troubleshooting the performance problems

Module 12: Spark SQL and Data Frames

Module 13: Scheduling/Partitioning

01-Dec-2025

Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches