Introduction to Spark with Python Training in Scottsdale

Enroll in or hire us to teach our Introduction to Spark with Python class in Scottsdale, Arizona by calling us @303.377.6176. Like all HSG classes, Introduction to Spark with Python may be offered either onsite or via instructor led virtual training. Consider looking at our public training schedule to see if it is scheduled: Public Training Classes
Provided there are enough attendees, Introduction to Spark with Python may be taught at one of our local training facilities.
We offer private customized training for groups of 3 or more attendees.

Course Description


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. All examples and labs use Python for programming.

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. DataFrames and Spark SQL). It includes in-depth coverage of Spark SQL and DataFrames, 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 pyspark 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.

Course Length: 3 Days
Course Tuition: $1090 (US)


Working knowledge of some programming language - no Java experience needed

Course Outline

Session 1: Introduction to Spark
Overview, Motivations, Spark Systems
Spark Ecosystem
Spark vs. Hadoop
Acquiring and Installing Spark
The Spark Shell, SparkContext
Session 2: RDDs and Spark Architecture
RDD Concepts, Lifecycle, Lazy Evaluation
RDD Partitioning and Transformations
Working with RDDs - Creating and Transforming (map, filter, etc.)
Session 3: Spark SQL, DataFrames, and DataSets
SparkSession, Loading/Saving Data, Data Formats (JSON, CSV, Parquet, text ...)
Introducing DataFrames (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
Mapping and Splitting (flatMap(), explode(), and split())
DataFrames vs. RDDs
Session 4: 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)
Session 5: Performance Tuning
Caching - Concepts, Storage Type, Guidelines
Minimizing Shuffling for Increased Performance
Using Broadcast Variables and Accumulators
General Performance Guidelines
Session 6: 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
Session 7: Spark Streaming
Introduction and Streaming Basics
Streaming Introduction
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

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