Spark Training

Contents

Course Preview
• Course overview
• Objectives

Introduction to Spark
• Limitations of MapReduce in Hadoop Objectives
• Batch vs. Real-time analytics
• Application of stream processing
• How to install Spark
• Spark vs. Hadoop Eco-system

Introduction to Programming in Scala
• Features of Scala
• Basic data types and literals used
• List the operators and methods used in Scala
• Concepts of Scala

Using RDD for Creating Applications in Spark
• Features of RDDs
• How to create RDDs
• RDD operations and methods
• How to run a Spark project with SBT
• Explain RDD functions and describe how to write different codes in Scala

SparkSQL
• Explain the importance and features of SparkSQL
• Describe methods to convert RDDs to DataFrames
• Explain concepts of SparkSQL
• Describe the concept of hive integration

Spark Streaming
• Concepts of Spark Streaming
• Describe basic and advanced sources
• Explain how stateful operations work
• Explain window and join operations

Spark ML Programming
• Explain the use cases and techniques of Machine Learning (ML)
• Describe the key concepts of Spark ML
• Explain the concept of an ML Dataset, and ML algorithm, model selection via cross validation

Spark GraphX Programming
• Explain the key concepts of Spark GraphX programming
• Limitations of the Graph Parallel system
• Describe the operations with a graph
• Graph system optimizations

Scala
• Scala - Environment Setup • Scala - Basic Syntax
• Scala - Data Types
• Scala - Variables
• Scala - Classes & Objects
• Scala - Access Modifiers
• Scala - Operators
• Scala - IF ELSE
• Scala - Loop Statements
• Scala - Functions
• Scala - Closures
• Scala - Strings
• Scala - Arrays
• Scala - Collections
• Scala - Traits
• Scala - Pattern Matching
• Scala - Regular Expressions
• Scala - Exception Handling
• Scala - Extractors
• Scala - Files I/O

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