Apache Spark Development training course

Module 1 - Introduction to Spark - Getting started

1. What is Spark and what is its purpose?
2.  Overview, Motivations, Spark Systems
3. Spark Ecosystem
4. Spark vs. Hadoop
5. Typical Spark Deployment and Usage Environments
6. Components of the Spark unified stack
7. Resilient Distributed Dataset (RDD)
8. Downloading and installing Spark standalone
9. Python overview
10. Launching and using the Python shell 

Module 2 - Resilient Distributed Dataset and DataFrames

1. Understand how to create parallelized collections and external datasets
2. Work with Resilient Distributed Dataset (RDD) operations
3. Utilize shared variables and key-value pairs
4. RDD Concepts, Partitions, Lifecycle, Lazy Evaluation
5. Working with RDDs - Creating and Transforming (map, filter, etc.)
6. Caching - Concepts, Storage Type, Guidelines
7.  Introduction and Usage
8. Creating and Using a DataSet
9. Working with JSON
10. Using the DataSet DSL
11. Using SQL with Spark
12. Data Formats
13. Optimizations: Catalyst and Tungsten
14. DataSets vs. DataFrames vs. RDDs

Module 3 - Spark application programming

1. Understand the purpose and usage of the SparkContext
2. Initialize Spark with the Python programming language
3. Describe and run some Spark examples
4. Pass functions to Spark
5. Create and run a Spark standalone application
6. Submit applications to the cluster
7. Overview, Basic Driver Code, SparkConf
8. Creating and Using a SparkContext/SparkSession
9. Building and Running Applications
10. Application Lifecycle
11. Cluster Managers
12. Logging and Debugging

Module 4 - Introduction to Spark libraries

1. Understand and use the various Spark libraries

Module 5 - Spark configuration, monitoring and tuning

1. Understand components of the Spark cluster
2. Configure Spark to modify the Spark properties, environmental variables, or logging properties
3. Monitor Spark using the web UIs, metrics, and external instrumentation
4. Understand performance tuning considerations
5. The Spark UI
6. Narrow vs. Wide Dependencies
7. Minimizing Data Processing and Shuffling
8. Caching - Concepts, Storage Type, Guidelines
9. Using Caching
10. Using Broadcast Variables and Accumulators

Module 6 - Spark STREAMING (optional)

1. Overview and Streaming Basics
2. Structured Streaming
3. DStreams (Discretized Steams),
4. Architecture, Stateless, Stateful, and Windowed Transformations
5. Spark Streaming API
6. Programming and Transformations