16 Hours Apache Spark Training Course in Lausanne
Event Information
Event description
16 Hours Apache Spark Training course is being delivered from April 27, 2021 - May 20, 2021 US Pacific Time.
About this Event
16 Hours Apache Spark Training course is being delivered from April 27, 2021 - May 20, 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
16 Hours Apache Spark Training Course Schedule
- April 27, 2021 - May 20, 2021 US Pacific time
- 4 Weeks | 2 Hours on Tuesdays, 2 Hours on Thursdays every week US Pacific time
- 8:30 AM - 10:30 AM US Pacific time each of those days
- Please click here to add your city name and check your local date and time for the first session to be held on April 27, 2021 at 8:30 AM 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:
- Exposure to knowledge of databases, SQL (Structured query language).
- Basic knowledge of object-oriented programming.
- Knowledge of Scala.
Course Objectives
- Apache Spark Architecture
- How to use Spark with Scala
- How to deploy Spark projects to the cloud Machine Learning with Spark
Course Outline
- Introduction to Spark
- Spark Vs MapReduce
- Spark Vs Hadoop
- Spark Installation, Configuration, Shell
- Spark and Resilient Distributed Datasets (RDD)
- Batch and Real-Time Analytics with Apache Spark
- Functional Programming
- Spark Architecture
- Collections
- Object-Oriented Programming
- Integrations
- Spark Core
- Persistence
- Cassandra (NoSQL database)
- Spark Integration with NoSQL (Cassandra) and Amazon EC2
- Spark Streaming
- Spark SQL
- Spark MLLib
- How to write and deploy Spark Applications
- Spark Parallel Processing
- Kafka
- Spark performance
- Scheduling and Partitioning
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