Training Courses

Getting Productive with Spark

Apache Spark is the next generation successor to MapReduce. Spark is a powerful, open source processing engine for data in the Hadoop cluster, optimized for speed, ease of use,and sophisticated analytics. The Spark framework supports streaming data processing and complex, iterative algorithms, enabling applications to run up to 100x faster than traditional Hadoop MapReduce programs.

The 2 day Spark course is aimed at developers who are encountering Spark for the first time and want to understand how to build Big Data Products with Spark. The course would enable participants to build complete, unified Big Data applications combining batch, streaming, and interactive analytics on all their data.

Developers would be able to write sophisticated parallel applications to execute faster decisions, better decisions, and real time actions, applied to a wide variety of use cases, architectures, and industries.

The course has a practical focus, mixing presentation with in depth hands on labs and exercises.

Day 1

Big Data Why and What?
Introduction to Spark.
Spark shell.
Programming with Spark.
Resilient Distributed Datasets.
RDD Operations.
Map Reduce.
Key value pair .
Jobs, Stages, Task
Web UI.
Stand alone cluster.
Building and running.
Partitions and Data Locality.
Executing parallel operations.
Caching Overview.
Distributed Persistence.

Day 2

Streaming operations.
Sliding window operations.
Streaming Applications.
Stateful Transformations.
Using HDFS with Spark.
Spark and MapReduce.
Spark Context.
Spark Properties.
Iterative Algorithms.
Graph Analysis.
Machine Learning.
Spark SQL.
Tunning Spark Application

Course Prerequisites

To benefit from this course you should have programming experience with Scala or with Python. The language of instruction is Scala. Basic Linux knowledge is expected.

For more information on the course or a discussion on your custom need, send a mail to