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Introduction to Spark Programming
Duration
4 Days
Course
DCSK-130
Available Formats
Classroom Training, Online Training
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
- Overview, Motivations, Spark Systems
- Spark Ecosystem
- Spark vs. Hadoop
- Acquiring and Installing Spark
- The Spark Shell, SparkContext
- RDD Concepts, Lifecycle, Lazy Evaluation
- RDD Partitioning and Transformations
- Working with RDDs - Creating and Transforming (map, filter, etc.)
- 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
- 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)
- Caching - Concepts, Storage Type, Guidelines
- Minimizing Shuffling for Increased Performance
- Using Broadcast Variables and Accumulators
- General Performance Guidelines
- 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
- 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
Related Courses
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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
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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