4 Weeks Apache Spark Training Course in Monterrey
Event Information
Event description
4 Weeks Apache Spark Training course is being delivered from February 23, 2021 - March 18, 2021 US Pacific Time.
About this Event
4 Weeks Apache Spark Training course is being delivered from February 23, 2021 - March 18, 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 Only Apache Spark Training Course Schedule
- February 23, 2021 - March 18, 2021 US Pacific time
- 4 Weeks | 2 hours on Tuesdays, 2 hours on Thursdays every week US Pacific time
- 5:30 PM - 7:30 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 February 23, 2021 at 5:30 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:
- 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