View the upcoming Apache Spark Training classes

4 Weeks Apache Spark Training course for Beginners is being delivered from May 11, 2021 - June 3 , 2021 US Pacific Time for 16 hours over 4 weeks, 8 sessions, 2 sessions per week, 2 hours per session.

• All Published Ticket Prices are in US Dollars
• The course will be taught in English language

4 Weeks Apache Spark Training Course Schedule

• May 11, 2021 - June 3 , 2021 US Pacific time
• 4 Weeks | 2 hours on Tuesdays, 2 hours on Thursdays every week US Pacific time
• 6:30 PM - 8:30 PM US Pacific time each of those days
• Please click here to add your location and check your local date and time for first session to be held on May 11, 2021 at 6:30 PM US Pacific Time.

Features and Benefits

• 4 weeks, 8 sessions, 16 hours of total Instructor-led and guided training
• Training material, instructor handouts and access to useful resources on the cloud provided
• Practical Hands-on Lab exercises provided
• Real-life Scenarios

Prerequisites

There are no prerequisites to join in our Apache Spark Training. However, having prior knowledge of following concepts will be an added advantage:

1. Exposure to knowledge of databases, SQL (Structured query language).
2. Basic knowledge of object-oriented programming.
3. Knowledge of Scala.

Course Objectives

1. Apache Spark Architecture
2. How to use Spark with Scala
3. How to deploy Spark projects to the cloud Machine Learning with Spark

Course Outline

1. Introduction to Spark
2. Spark Vs MapReduce
3. Spark Vs Hadoop
4. Spark Installation, Configuration, Shell
5. Spark and Resilient Distributed Datasets (RDD)
6. Batch and Real-Time Analytics with Apache Spark
7. Functional Programming
8. Spark Architecture
9. Collections
10. Object-Oriented Programming
11. Integrations
12. Spark Core
13. Persistence
14. Cassandra (NoSQL database)
15. Spark Integration with NoSQL (Cassandra) and Amazon EC2
16. Spark Streaming
17. Spark SQL
18. Spark MLLib
19. How to write and deploy Spark Applications
20. Spark Parallel Processing
21. Kafka
22. Spark performance
23. Scheduling and Partitioning