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Introduction to Spark Programming

Price
2,800 USD
4 Days
DCSK-130
Classroom Training, Online Training
Other

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Course Description

Overview

This course introduces the Apache Spark distributed computing engine, and is suitable for developers, data analysts, architects, technical managers, and anyone who needs to use Spark in a hands-on manner. It is based on the Spark 2.x release.
The course provides a solid technical introduction to the Spark architecture and how Spark works. It covers the basic building blocks of Spark (e.g. RDDs and the distributed compute engine), as well as higher-level constructs that provide a simpler and more capable interface (e.g. DataSets/DataFrames and Spark SQL). It includes in-depth coverage of Spark SQL, DataFrames, and DataSets, which are now the preferred programming API. This includes exploring possible performance issues and strategies for optimization.
The course also covers more advanced capabilities such as the use of Spark Streaming to process streaming data, and integrating with the Kafka server.
The course is very hands-on, with many labs. Participants will interact with Spark through the Spark shell (for interactive, ad-hoc processing) as well as through programs using the Spark API. After taking this course, you will be ready to work with Spark in an informed and productive manner.
Labs are supported in both Python and Scala

 

Objectives

Upon completion of the Introduction to Spark Programming course, students will be able to:
  • Know the difference between “data-at-rest” and “data-in-motion”
  • Understand what map-reduce / Hadoop is, and what it can do
  • Be aware of query technologies for easily querying with Hadoop (e.g. Hive, Pig, and others)
  • Understand what NoSQL databases are and what they can do
  • Become familiar with the choices in the NoSQL landscape
  • Understand the strengths and weaknesses of different NoSQL technologies
  • Be well-informed on your choices in Big Data processing, and evaluate them for your needs
  • Understand what Big Data is

Audience

Developers, data analysts, architects, technical managers, and anyone who needs to use Spark in a hands-on manner
 

Prerequisites

    Working knowledge of some programming language - no Java experience needed
     

Topics

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
2. Introduction to Spark
  • Overview, Motivations, Spark Systems
  • Spark Ecosystem
  • Spark vs. Hadoop
  • Acquiring and Installing Spark
  • The Spark Shell, SparkContext
3. RDDs and Spark Architecture
  • RDD Concepts, Lifecycle, Lazy Evaluation
  • RDD Partitioning and Transformations
  • Working with RDDs - Creating and Transforming (map, filter, etc.)
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
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)
6. Performance Tuning
  • Caching - Concepts, Storage Type, Guidelines
  • Minimizing Shuffling for Increased Performance
  • Using Broadcast Variables and Accumulators
  • General Performance Guidelines
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
8. 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
  • Spark V2 for Developers

    DCSK-100
    • Duration: 3 Days
    • Delivery Format: Classroom Training, Online Training
    • Price: 2,100.00 USD
  • Solr for Developers

    SESR-100
    • Duration: 3 Days
    • Delivery Format: Classroom Training, Online Training
    • Price: 2,100.00 USD
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