Introduction to Spark Programming - 4 Day Training in Washington D C, Maryland

Session 1 (Optional): Scala Ramp Up

• Scala Introduction, Variables, Data Types, Control Flow
• The Scala Interpreter
• Collections and their Standard Methods (e.g. map())
• Functions, Methods, Function Literals
• Class, Object, Trait, case Class

Session 2: Introduction to Spark

• Overview, Motivations, Spark Systems
• Spark Ecosystem
• Spark vs. Hadoop
• Acquiring and Installing Spark
• The Spark Shell, SparkContext

Session 3: RDDs and Spark Architecture

• RDD Concepts, Lifecycle, Lazy Evaluation
• RDD Partitioning and Transformations
• Working with RDDs - Creating and Transforming (map, filter, etc.)

Session 4: Spark SQL, DataFrames, and DataSets

• Overview
• SparkSession, Loading/Saving Data, Data Formats (JSON, CSV, Parquet, text ...)
• Introducing DataFrames and DataSets (Creation and Schema Inference)
• Supported Data Formats (JSON, Text, CSV, Parquet)
• Working with the DataFrame (untyped) Query DSL (Column, Filtering, Grouping, Aggregation)
• SQL-based Queries
• Working with the DataSet (typed) API
• Mapping and Splitting (flatMap(), explode(), and split())
• DataSets vs. DataFrames vs. RDDs

Session 5: Shuffling Transformations and Performance

• Grouping, Reducing, Joining
• Shuffling, Narrow vs. Wide Dependencies, and Performance Implications
• Exploring the Catalyst Query Optimizer (explain(), Query Plans, Issues with lambdas)
• The Tungsten Optimizer (Binary Format, Cache Awareness, Whole-Stage Code Gen)

Session 6: Performance Tuning

• Caching - Concepts, Storage Type, Guidelines
• Minimizing Shuffling for Increased Performance
• Using Broadcast Variables and Accumulators
• General Performance Guidelines

Session 7: Creating Standalone Applications

• Core API, SparkSession.Builder
• Configuring and Creating a SparkSession
• Building and Running Applications - sbt/build.sbt and spark-submit
• Application Lifecycle (Driver, Executors, and Tasks)
• Cluster Managers (Standalone, YARN, Mesos)
• Logging and Debugging

Session 7: Spark Streaming

• Introduction and Streaming Basics
• Spark Streaming (Spark 1.0+)
  • DStreams, Receivers, Batching
  • Stateless Transformation
  • Windowed Transformation
  • Stateful Transformation
• Structured Streaming (Spark 2+)
  • Continuous Applications
  • Table Paradigm, Result Table
  • Steps for Structured Streaming
  • Sources and Sinks
• Consuming Kafka Data
  • Kafka Overview
  • Structured Streaming - "kafka" format
  • Processing the Stream