Cloudera Developer Training for Apache Spark and Hadoop

This course is designed for:

• developers who have programming experience
• engineers who have programming experience

Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning skills such as:

• How the Apache Hadoop ecosystem fits in with the data processing lifecycle
• How data is distributed, stored, and processed in a Hadoop cluster
• How to write, configure, and deploy Apache Spark applications on a Hadoop cluster
• How to use the Spark shell and Spark applications to explore, process, and analyze distributed data
• How to query data using Spark SQL, DataFrames, and Datasets
• How to use Spark Streaming to process a live data stream

• Apache Spark examples and hands-on exercises are presented in Scala and Python. The ability to program in one of those languages is required.
• Basic familiarity with the Linux command line is assumed.
• Basic knowledge of SQL is helpful.
• Prior knowledge of Hadoop and/or Spark is not required.

The supply of this course by DDLS is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.

• Introduction to Apache Hadoop & the Hadoop Ecosystem
• Apache Hadoop File Storage
• Distributed Processing on an Apache Hadoop Cluster
• Apache Spark Basics
• Working with DataFrames and Schemas
• Analysing Data with DataFrame Queries
• RDD Overview
• Transforming Data with RDDs
• Aggregating Data with Pair RDDs
• Querying Tables and Views with Apache Spark SQL
• Working with Datasets in Scala
• Writing, Configuring & Running Apache Spark Applications
• Spark Distributed Processing
• Distributed Data Persistence
• Common Patterns in Apache Spark Data Processing
• Introduction to Structured Streaming
• Structured Streaming with Apache Kafka
• Aggregating and Joining Streaming DataFrames

1. Introduction

2. Introduction to Apache Hadoop and the Hadoop Ecosystem

• Apache Hadoop Overview
• Data Processing
• Introduction to the Hands-On Exercises

3. Apache Hadoop File Storage

• Apache Hadoop Cluster Components
• HDFS Architecture
• Using HDFS

4. Distributed Processing on an Apache Hadoop Cluster

• YARN Architecture
• Working With YARN

5. Apache Spark Basics

• What is Apache Spark?
• Starting the Spark Shell
• Using the Spark Shell
• Getting Started with Datasets and DataFrames
• DataFrame Operations

6. Working with DataFrames and Schemas

• Creating DataFrames from Data Sources
• Saving DataFrames to Data Sources
• DataFrame Schemas
• Eager and Lazy Execution

7. Analyzing Data with DataFrame Queries

• Querying DataFrames Using Column Expressions
• Grouping and Aggregation Queries
• Joining DataFrames

8. RDD Overview

• RDD Data Sources
• Creating and Saving RDDs
• RDD Operations

9. Transforming Data with RDDs

• Writing and Passing Transformation Functions
• Transformation Execution
• Converting Between RDDs and DataFrames

10. Aggregating Data with Pair RDDs

• Key-Value Pair RDDs
• Map-Reduce
• Other Pair RDD Operations

11. Querying Tables and Views with SQL

• Querying Tables in Spark Using SQL
• Querying Files and Views
• The Catalog API

12. Working with Datasets in Scala

• Datasets and DataFrames
• Creating Datasets
• Loading and Saving Datasets
• Dataset Operations

13. Writing, Configuring, and Running Spark Applications

• Writing a Spark Application
• Building and Running an Application
• Application Deployment Mode
• The Spark Application Web UI
• Configuring Application Properties

14. Spark Distributed Processing

• Review: Apache Spark on a Cluster
• RDD Partitions
• Example: Partitioning in Queries
• Stages and Tasks
• Job Execution Planning
• Example: Catalyst Execution Plan
• Example: RDD Execution Plan

15. Distributed Data Persistence

• DataFrame and Dataset Persistence
• Persistence Storage Levels
• Viewing Persisted RDDs

16. Common Patterns in Spark Data Processing

• Common Apache Spark Use Cases
• Iterative Algorithms in Apache Spark
• Machine Learning
• Example: k-means

17. Introduction to Structured Streaming

• Apache Spark Streaming Overview
• Creating Streaming DataFrames
• Transforming DataFrames
• Executing Streaming Queries

18. Structured Streaming with Apache Kafka

• Overview
• Receiving Kafka Messages
• Sending Kafka Messages

19. Aggregating and Joining Streaming DataFrames

• Streaming Aggregation
• Joining Streaming DataFrames

20. Conclusion A. Message Processing with Apache Kafka

• What Is Apache Kafka?
• Apache Kafka Overview
• Scaling Apache Kafka
• Apache Kafka Cluster Architecture
• Apache Kafka Command Line Tools