How to work with RDD in Spark

This web-based training course on How to work with RDD in Spark functionality, administration and development, is available online to all individuals, institutions, corporates and enterprises in India (New Delhi NCR, Bangalore, Chennai, Kolkatta), US, UK, Canada, Australia, Singapore, United Arab Emirates (UAE), China and South Africa. No matter where you are located, you can enroll for any training with us - because all our training sessions are delivered online by live instructors using interactive, intensive learning methods.

Apache Spark is an extremely fast data processing tool which is designed for quick computations and is built on top of the Hadoop MapReduce platform. It extends the MapReduce model for various other types of manipulations including interactive queries and stream processing. RDD or resilient distributed datasets are the fundamental data structures of spark which is inherently a distributed collection of objects. Every dataset of RDD is segmented into various logical partitions which can be manipulated and computed on various different nodes of the cluster. The unique feature of RDDs is that any type of python, java or scala objects can be implemented in it including user-defined classes. RDD is read-only and partitioned collection of records which can be created through deterministic operations on either data or stable storages or other RDDs too. With RDDs, you gain a collection of elements which are not just fault tolerant but can also be operated in parallel.

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Course Details

Through this Apache Spark RDD online training course, in-depth information about the architecture of Spark along with the concepts of data distribution and parallel task execution will be provided. The training provides the requisite knowledge of optimizing data for joins with the help of Spark’s memory caching. This RDD in Spark online training course further provides information of working with advanced operations available in the Spark API and also provides lab and practical exercises for operating on the cloud using the notebook interface. This course on working with RDD in Spark requires no pre-requisites but it is advised that the trainees have a basic understanding of data management to keep pace with the course. Knowledge of Hadoop will be an added advantage to successfully complete this course.

Introduction to Notebooks

  • Methods of using Zeppelin in the customized Spark projects
  • Working with various notebooks in Spark

The Spark RDD Architecture

  • Understanding RDDs
  • The synchronization of RDDs on Spark
  • Understanding the ways in which Spark generates RDDs
  • Manage partitions to improve RDD performance

Ways of Optimizing Transformations and Actions

  • Optimization techniques
  • Actions associated with RDD and Spark
  • Using advanced Spark RDD operations
  • Identification of operations which cause shuffling

Caching and Serialization techniques

  • Why is caching needed?
  • Why is serialization needed?
  • Scenarios in which Caching of RDDs is done
  • The various storage levels and how to implement them

Development and Testing Techniques

  • Understanding the use of sbt to build Spark projects
  • Understanding the use of Eclipse and IntelliJ for Spark development

Live Instructor-led & Interactive Online Sessions

Regular Course

Duration : 40 Hours

Capsule Course

Duration : 4-8 Hours

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Training Options


Weekdays- Cloud Based Training

Mon - Fri 07:00 AM - 09:00 AM(Mon, Wed, Fri)

Weekdays Online Lab

Mon - Fri 07:00 AM - 09:00 AM(Tue, Thur)


Weekend- Cloud Based Training

Sat-Sun 09:00 AM - 11:00 AM (IST)

Weekend Online Lab

Sat-Sun 11:00 AM - 01:00 PM

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