Cloudera Developer Training for Apache Spark

• Developers
• Data Engineers

• Course examples and exercises are presented in Python and Scala, so knowledge of one of these programming languages is required.
• Basic knowledge of Linux is assumed.

By the end of this course, you will learn:

• Using the Spark shell for interactive data analysis
• The features of Spark’s Resilient Distributed Datasets
• How Spark runs on a cluster
• How Spark parallelizes task execution
• Writing Spark applications
• Processing streaming data with Spark

This three-day course for Apache Spark enables you to build complete, unified big data applications combining batch, streaming, and interactive analytics on all their data. With Spark, developers can write sophisticated parallel applications to execute faster decisions, better decisions, and real-time actions, applied to a wide variety of use cases, architectures and industries.

Advance Your Ecosystem Expertise

Apache Spark is the next-generation successor to MapReduce. Spark is a powerful, opensource processing engine for data in the Hadoop cluster, optimized for speed, ease of use, and sophisticated analytics. The Spark framework supports streaming data processing and complex, iterative algorithms, enabling applications to run up to 100x faster than traditional Hadoop MapReduce programs.

Module 1: Introduction to Spark

• What is Spark?
• Review: From Hadoop MapReduce to Spark
• Review: HDFS
• Review: YARN
• Spark Overview

Module 2: Spark Basics

• Using the Spark Shell
• RDDs (Resilient Distributed Datasets)
• Functional Programming in Spark

Module 3: Working with RDDs in Spark

• Creating RDDs
• Other General RDD Operations

Module 4: Aggregating Data with Pair RDDs

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

Module 5: Writing and Deploying Spark Applications

• Spark Applications vs. Spark Shell
• Creating the SparkContext
• Building a Spark Application (Scala and Java)
• Running a Spark Application
• The Spark Application Web UI
• Hands-On Exercise: Write and Run a Spark Application
• Configuring Spark Properties
• Logging

Module 6: Parallel Processing

• Review: Spark on a Cluster
• RDD Partitions
• Partitioning of File-based RDDs
• HDFS and Data Locality
• Executing Parallel Operations
• Stages and Tasks

Module 7: Spark RDD Persistence

• RDD Lineage
• RDD Persistence Overview
• Distributed Persistence

Module 8: Basic Spark Streaming

• Spark Streaming Overview
• Example: Streaming Request Count
• DStreams
• Developing Spark Streaming Applications

Module 9: Advanced Spark Streaming

• Multi-Batch Operations
• State Operations
• Sliding Window Operations
• Advanced Data Sources

Module 10: Common Patterns in Spark Data Processing

• Common Spark Use Cases
• Iterative Algorithms in Spark
• Graph Processing and Analysis
• Machine Learning
• Example: k-means

Module 11: Improving Spark Performance

• Shared Variables: Broadcast Variables
• Shared Variables: Accumulators
• Common Performance Issues
• Diagnosing Performance Problems

Module 12: Spark SQL and DataFrames

• Spark SQL and the SQL Context
• Creating DataFrames
• Transforming and Querying DataFrames
• Saving DataFrames
• DataFrames and RDDs
• Comparing Spark SQL, Impala and Hive-on-Spark