Cloudera Training for Apache HBase

Take your knowledge to the next level with Cloudera Training for Apache HBase. Cloudera Educational Services’ three-day training course enables participants to store and access massive quantities of multi-structured data and perform hundreds of thousands of operations per second.

Who Can Benefit

• This course is appropriate for developers and administrators who intend to use HBase.

Skills Gained

• During this course, you will learn how to:
• Determine whether HBase is appropriate for a given use case
• Employ best practices for schema and row key design
• Create, populate, alter, and remove HBase tables
• Add, locate, retrieve, update, and delete data
• Perform common cluster administration tasks
• Identify and resolve performance bottlenecks

Prerequisites

• Prior experience with databases and data modeling is helpful, but not required. Knowledge of Java is assumed. Prior knowledge of Hadoop is not required, but Cloudera Developer Training for Spark and Hadoop provides an excellent foundation for this course.

Course Content

Introduction to Hadoop and HBase

• Introducing Hadoop
• Core Hadoop Components
• Exercise: Using HDFS
• What is HBase?
• Strengths of HBase
• HBase in Production
• Weaknesses of HBase

HBase Tables

• HBase Concepts
• HBase Table Fundamentals
• Thinking About Table Design
• Exercise: HBase Data Import

HBase Shell

• Creating Tables with the HBase Shell
• Working with Tables
• Exercise: Using the HBase Shell
• Working with Table Data
• Exercise: Data Access in the HBase Shell

HBase Architecture Fundamentals

• HBase Regions
• HBase Cluster Architecture
• HBase and HDFS Data Locality

HBase Schema Design

• General Design Considerations
• Application-Centric Design
• Designing HBase Row Keys
• Other HBase Table Features
• Exercise: Using MIN_VERSIONS and Time-To-Live
• General Design Considerations
• Application-Centric Design
• Designing HBase Row Keys

Basic Data Access with the HBase API

• Options to Access HBase Data
• Creating and Deleting HBase Tables
• Retrieving Data with Get
• Retrieving Data with Scan
• Inserting and Updating Data
• Deleting Data
• Exercise: Using the Developer API

More Advanced HBase API Features

• Filtering Scans
• Exercise: HBase Filters
• Client-Side Write Buffer
• Exercise: Using Client-Side Write Buffer
• Best Practices
• HBase Coprocessors
• Exercise: Using Atomic Counters

HBase Write Path

• HBase Write Path
• Exercise: Exploring HBase
• Compaction
• Splits
• Exercise: Flushes and Compactions

HBase Read Path

• How HBase Reads Data
• Block Caches for Reading

HBase Performance Tuning

• Column Family Considerations
• Schema Design Considerations
• Configuring for Caching
• Memory Considerations
• Dealing with Time Series and Sequential Data
• Pre-Splitting Regions
• Exercise: DetectingHot Spots

HBase Administration and Cluster Management

• HBase Schema Design
• General Design Considerations
• Application-Centric Design
• Designing HBase Row Keys

HBase Replication and Backup

• HBase Replication
• HBase Backup
• MapReduce and HBase Clusters
• Exercise: Administration

Using Hive and Impala with HBase

• How to use Hive and Impala to Access HBase
• Exercise: Hive and HBase

Appendix A: Accessing Data with Python and Thrift

• Thrift Usage
• Working with Tables
• Getting and Putting Data
• ScanningData
• Deleting Data
• Counters
• Filters
• Optional Exercise: Using Python and Thrift with HBase

Appendix B: OpenTSDB

Appendix C: hbase-spark API

• Introduction
• Architecture and Integration Patterns
• Typing and API Usage
• Future Work
• Optional Exercise: Using hbase-sparkAPI