Spark for Developers ( sparkdev | 21 hours )

PRE-REQUISITES

familiarity with either Java / Scala / Python language (our labs in Scala and Python)
basic understanding of Linux development environment (command line navigation / editing files using VI or nano)

OBJECTIVE:

This course will introduce Apache Spark. The students will learn how  Spark fits  into the Big Data ecosystem, and how to use Spark for data analysis.  The course covers Spark shell for interactive data analysis, Spark internals, Spark APIs, Spark SQL, Spark streaming, and machine learning and graphX.

AUDIENCE :

Developers / Data Analysts

1. Scala primer

   • A quick introduction to Scala
   • Labs : Getting know Scala

2. 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

3. First Look at Spark

   • Running Spark in local mode
   • Spark web UI
   • Spark shell
   • Analyzing dataset – part 1
   • Inspecting RDDs
   • Labs: Spark shell exploration

4. 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

5. 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

6. 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

7. MLlib

   • MLlib intro
   • MLlib algorithms
   • Labs : Writing MLib applications

8. GraphX

   • GraphX library overview
   • GraphX APIs
   • Labs : Processing graph data using Spark

9. Spark Streaming

   • Streaming overview
   • Evaluating Streaming platforms
   • Streaming operations
   • Sliding window operations
   • Labs : Writing spark streaming applications

10. Spark and Hadoop

    • Hadoop Intro (HDFS / YARN)
    • Hadoop + Spark architecture
    • Running Spark on Hadoop YARN
    • Processing HDFS files using Spark

11. Spark Performance and Tuning

    • Broadcast variables
    • Accumulators
    • Memory management & caching

12. Spark Operations

    • Deploying Spark in production
    • Sample deployment templates
    • Configurations
    • Monitoring
    • Troubleshooting