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Cloudera Training for Apache HBase (CAHB)
New Age Technologies has been delivering Authorized Training since 1996. We offer Cloudera’s full suite of authorized courses including courses pertaining to Apache Spark, Hadoop, Apache HBase, MapReduce, Data Science, Cloudera Developer and more. If you have any questions or can’t seem to find the Cloudera class that you are interested in, contact one of our Cloudera Training Specialists. Invest in your future today with Cloudera training from New Age Technologies.
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Cloudera Training for Apache HBase Overview:
The Cloudera Training for Apache HBase four-day training course enables you to store and access massive quantities of multi-structured data and perform hundreds of thousands of operations per second. This course is part of both the developer learning path and the administrator learning path.
Cloudera’s Hadoop Ecosystem -> Apache HBase:
Apache HBase is a distributed, scalable, NoSQL database built on Apache Hadoop. HBase can store data in massive tables consisting of billions of rows and millions of columns, serve data to many users and applications in real time, and provide fast, random read/write access to users and applications.
Cloudera Training for Apache HBase Prerequisites:
Before attending this course, you must have the following:
- Developers and administrators who intend to use HBase
- Prior experience with databases and data modeling is helpful, but not required
- Prior knowledge of Java
- Cloudera Developer Training for Apache Hadoop provides an excellent foundation for this course
Cloudera Training for Apache HBase Objectives:
After successfully completing this course, you will learn such topics as:
- 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, configure the HBase cluster, and administer an HBase cluster
- Best practices for identifying and resolving performance bottlenecks
Cloudera Training for Apache HBase Certification
- Cloudera Certified Specialist in Apache HBase
Cloudera Training for Apache HBase Outline:
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
Appendix A: Accessing Data with Python and Thrift
- Thrift Usage
- Working with Tables
- Getting and Putting Data
- Scanning Data
- Deleting Data
- Counters
- Filters
