Apache Spark Training Course
- Problems with Traditional Large-Scale Systems
- Introducing Spark
- What is Apache Spark?
- Using the Spark Shell
- Resilient Distributed Datasets (RDDs)
- Functional Programming with Spark
Working with RDDs
- RDD Operations
- Key-Value Pair RDDs
- MapReduce and Pair RDD Operations
The Hadoop Distributed File System
- Why HDFS?
- HDFS Architecture
- Using HDFS
Running Spark on a Cluster
- A Spark Standalone Cluster
- The Spark Standalone Web UI
Parallel Programming with Spark
- RDD Partitions and HDFS Data Locality
- Working With Partitions
- Executing Parallel Operations
Caching and Persistence
- RDD Lineage
- Caching Overview
- Distributed Persistence
Writing Spark Applications
- Spark Applications vs. Spark Shell
- Creating the SparkContext
- Configuring Spark Properties
- Building and Running a Spark Application
Spark, Hadoop, and the Enterprise Data Center
- Spark and the Hadoop Ecosystem
- Spark and MapReduce
- Spark Streaming Overview
- Example: Streaming Word Count
- Other Streaming Operations
- Sliding Window Operations
- Developing Spark Streaming Applications
Common Spark Algorithms
- Iterative Algorithms
- Graph Analysis
- Machine Learning
Improving Spark Performance
- Shared Variables: Broadcast Variables
- Shared Variables: Accumulators
- Common Performance Issues
Bookings, Prices and Enquiries
Guaranteed to run even with a single delegate!
Public ClassroomParticipants from multiple organisations. Topics usually cannot be customised
Private ClassroomParticipants are from one organisation only. No external participants are allowed. Usually customised to a specific group, course topics are agreed between the client and the trainer.
Private RemoteThe instructor and the participants are in two different physical locations and communicate via the Internet
The more delegates, the greater the savings per delegate. Table reflects price per delegate and is used for illustration purposes only, actual prices may differ.
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