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Course Overview
This four-day hands-on training course delivers the key concepts and expertise developers need to use Apache Spark to develop high-performance parallel applications. Participants will learn how to use Spark SQL to query structured data and Spark Streaming to perform real-time processing on streaming data from a variety of sources. Developers will also practice writing applications that use core Spark to perform ETL processing and iterative algorithms. The course covers how to work with “big data” stored in a distributed file system, and execute Spark applications on a Hadoop cluster. After taking this course, participants will be prepared to face real-world challenges and build applications to execute faster decisions, better decisions, and interactive analysis, applied to a wide variety of use cases, architectures, and industries.
Who Should Attend
This course is designed for developers and engineers who have programming experience, but prior knowledge of Hadoop and/or Spark is not required.
Course Outline
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 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
- Querying Tables in Spark Using SQL
- Querying Files and Views
- The Catalog API
- Comparing Spark SQL, Apache Impala, and Apache Hive-on-Spark
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
- Message Processing with Apache Kafka