APACHE-SPARK

Course overview

This course delivers the key concepts and expertise developers need to use Apache Spark to develop high-performance parallel applications. Participants will learn how to use Spark Core and Spark SQL to query structured data and Spark Streaming to perform real-time processing on streaming data from a variety of sources.

Developers will also practice writing applications that use core Spark to perform ETL processing and iterative algorithms. The course covers how to work with “big data” stored in a distributed file system and execute Spark applications on a Hadoop cluster. After taking this course, participants will be prepared to face real-world challenges and build applications to execute faster decisions, better decisions, and interactive analysis, applied to a wide variety of use cases, architectures, and industries.

Course Duration

5 Days

Cost

Audience

Anyone who knows SQL.

Prerequisites

Basic understanding of distributed frameworks and any object-oriented language.

Course Content

  1. Scala primer 
  • A quick introduction to Scala 
  • Labs : Getting know Scala 
  1. Spark Basics 
  • Background and history 
  • Spark and Hadoop 
  • Spark concepts and architecture
  • Spark eco system (core, spark sql, mlib, streaming) 
  • Labs : Installing and running Spark
  1. First Look at Spark 
  • Running Spark in local mode
  • Spark web UI 
  • Spark shell 
  • Analyzing dataset – part 1
  • Inspecting RDDs 
  • Labs: Spark shell exploration
  1. RDDs 
  • RDDs concepts
  • Partitions 
  • RDD Operations / transformations
  • RDD types
  • Key-Value pair RDDs
  • MapReduce on RDD
  • Caching and persistence
  • Labs : creating & inspecting RDDs; Caching RDDs 
  1. Spark API programming 
  • Introduction to Spark API / RDD API
  • Submitting the first program to Spark
  • Debugging / logging 
  • Configuration properties
  • Labs : Programming in Spark API, Submitting jobs
  1. Spark SQL 
  • SQL support in Spark
  • Dataframes
  • Defining tables and importing datasets
  • Querying data frames using SQL 
  • Storage formats : JSON / Parquet
  • Labs : Creating and querying data frames; evaluating data formats 
  1. MLlib 
  • MLlib intro 
  • MLlib algorithms
  • Labs : Writing MLib applications 
  1. GraphX 
  • GraphX library overview
  • GraphX APIs
  • Labs : Processing graph data using Spark
  1. Spark Streaming 
  • Streaming overview
  • Evaluating Streaming platforms
  • Streaming operations
  • Sliding window operations
  • Labs : Writing spark streaming applications 
  1. Spark and Hadoop 
  • Hadoop Intro (HDFS / YARN)
  • Hadoop + Spark architecture
  • Running Spark on Hadoop YARN
  • Processing HDFS files using Spark
  1. Spark Performance and Tuning 
  • Broadcast variables
  • Accumulators 
  • Memory management & caching
  1. Spark Operations 
  • Deploying Spark in production
  • Sample deployment templates
  • Configurations 
  • Monitoring
  • Troubleshooting

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