Cloudera Training for Apache HBase

• Developers
• Administrators
• Engineers

• Prior experience with databases and data modeling is helpful
• Knowledge of Java
• Cloudera Developer Training for Apache Hadoop provides an excellent foundation for this course.

By the end of this course, you will learn:

• The use cases and usage occasions for HBase, Hadoop, and RDBMS
• Using the HBase shell to directly manipulate HBase tables
• Designing optimal HBase schemas for efficient data storage and recovery
• How to connect to HBase using the Java API to insert and retrieve data in real time
• Best practices for identifying and resolving performance bottlenecks

Cloudera University’s three-day training course for Apache HBase enables participants to store and access massive quantities of multi-structured data and perform hundreds of thousands of operations per second.Through instructor-led discussion and interactive, hands-on exercises, you will learn to navigate the Hadoop ecosystem.

Module 1: Introduction to Hadoop and HBase

• What Is Big Data?
• Introducing Hadoop
• Hadoop Components
• What Is HBase?
• Why Use HBase?
• Strengths of HBase
• HBase in Production
• Weaknesses of HBase

Module 2: HBase Tables

• HBase Concepts
• HBase Table Fundamentals
• Thinking About Table Design

Module 3: The HBase Shell

• Creating Tables with the HBase Shell
• Working with Tables
• Working with Table Data

Module 4: HBase Architecture Fundamentals

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

Module 5: HBase Schema Design

• General Design Considerations
• Application-Centric Design
• Designing HBase Row Keys
• Other HBase Table Features

Module 6: 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

Module 7: More Advanced HBase API Features

• Filtering Scans
• Best Practices
• HBase Coprocessors

Module 8: HBase on the Cluster

• How HBase Uses HDFS
• Compactions and Splits

Module 9: HBase Reads and Writes

• How HBase Writes Data
• How HBase Reads Data
• Block Caches for Reading

Module 10: HBase Performance Tuning

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

Module 11: HBase Administration and Cluster Management

• HBase Daemons
• ZooKeeper Considerations
• HBase High Availability
• Using the HBase Balancer
• Fixing Tables with hbck
• HBase Security

Module 12: HBase Replication and Backup

• HBase Replication
• HBase Backup
• MapReduce and HBase Clusters

Module 13: Using Hive and Impala with HBase

• Using Hive and Impala with HBase

Module 14: Appendix A: Accessing Data with Python and Thrift

• Thrift Usage
• Working with Tables
• Getting and Putting Data
• Scanning Data
• Deleting Data
• Counters
• Filters

Module 15: Appendix B: OpenTSDB