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HBase

HBase

Duration
45 Hours

Course Description

         HBase is a distributed, NoSQL database built on top of Hadoop, offering real-time read/write access to large datasets. It's column-oriented, meaning data is stored in columns rather than rows, and it's designed to handle massive amounts of data with billions of rows and millions of columns. HBase is known for its scalability, high throughput, and ability to handle sparse datasets efficiently. 

Course Outline For HBase

1. Introduction to HBase and NoSQL

  • Understanding Big Data and Hadoop: What is Big Data, the role of Hadoop, HDFS (Hadoop Distributed File System), and MapReduce.
  • Introduction to NoSQL: The need for NoSQL databases, their features, and how they differ from traditional relational databases (RDBMS).
  • What is Apache HBase?: HBase as an open-source, distributed, column-oriented NoSQL database modeled after Google's Bigtable, built on top of HDFS.
  • HBase Use Cases: When to use HBase for real-time read/write access to large datasets, including scenarios like online log statistics, compliance reports, and handling massive data volumes, 
  • Comparison of HBase with HDFS and RDBMS: Understanding the strengths and weaknesses of each technology and when to choose HBase. 

2. HBase Architecture

  • Core Components: HMaster, RegionServers, ZooKeeper, and their roles in the cluster.
  • Regions and RegionServers: Understanding how tables are split into regions and served by RegionServers.
  • ZooKeeper: Its role in coordination, synchronization, and handling server failures.
  • HBase Read and Write Operations: Understanding the flow of data during read and write processes, including MemStore and StoreFiles.
  • Compaction: The process of combining HFiles to optimize storage and read performance.
  • Auto Sharding: How HBase automatically distributes tables into regions for scalability. 

3. HBase Data model and schema design

  • Understanding HBase Data Hierarchy: Tables, rows, column families, columns, and cells.
  • RowKey: Its importance in identifying rows and its impact on performance.
  • Column Families: Their role in organizing data and storing related columns together.
  • Designing Optimal Schemas: Best practices for creating efficient HBase schemas based on application requirements, 
  • Timestamp as Versions: How HBase handles multiple versions of data using timestamps. 

4. HBase operations

  • HBase Shell: Using the HBase Shell for creating, modifying, and deleting tables, and performing basic data manipulation operations,
  • HBase Client API (Java): Developing Java applications to interact with HBase, including CRUD (Create, Read, Update, Delete) operations, and advanced features like filters, counters, and batch operations.
  • Data Loading: Loading data into HBase from various sources using tools like Sqoop, Pig, and Hive.
  • Querying Techniques: Retrieving data using Get, Scan, and Filters.
  • Advanced Operations: Exploring advanced functionalities like counters and data manipulation techniques. 

5. HBase performance tuning and administration

  • Performance Bottlenecks: Identifying and resolving common performance issues in HBase.
  • Tuning Techniques: Strategies for optimizing HBase performance, including schema design, memory management, caching, and scan optimization.
  • Cluster Management: Understanding the responsibilities of the HMaster and RegionServers in managing the cluster.
  • Monitoring and Troubleshooting: Tools and techniques for monitoring HBase cluster health and troubleshooting issues, including Cloud Monitoring and Logging.
  • Replication and Backup: Strategies for ensuring data availability and disaster recovery. 

6. Integration with Hadoop ecosystem

  • HBase and MapReduce: Integrating HBase with MapReduce jobs for data processing.
  • HBase and Hive: Leveraging Hive for SQL-like queries and analytics on HBase data, 
  • HBase and Spark: Understanding how HBase integrates with Spark for distributed data processing.
  • HBase and Impala: Using Impala for real-time querying and analytics on HBase data, 
  • Using HBase with Cloud Platforms: Exploring how HBase integrates with cloud services and containerization tools like Kubernetes, 
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