Apache HBase : Hadoop Column Oriented NoSQL Database

Explore the Hadoop framework, an open source Java platform for distributed processing of massive data sets across clusters of commodity hardware using simple programming and map reduce programming.

Explore how hdfs, a distributed file system, enables high-performance data access across clusters by splitting files into blocks replicated on data nodes under a name node master for fault tolerance.

Explore how hadoop mapreduce enables distributed processing of large data sets on commodity clusters by dividing input into subproblems, mapping data to key-value pairs, and reducing to aggregated results.

Compare hdfs and hbase: hdfs is fault-tolerant and high-throughput for large data with limited random lookups; hbase provides random, low-latency access to large tables on hdfs using column-oriented storage.

Use HBase for millions to billions of rows; for few thousand rows, a DBMS may be better, since HBase is not a replacement for relational databases and lacks join optimization.

Explore the building blocks of Apache HBase: tables, rows, columns, cells, and column families, and see how region servers and regions organize data and support API operations.

Explore how a column family groups related columns under a prefix and stores them together, with cells identified by family and qualifier and defaulting to the latest timestamped value.

HBase stores data on disk by column family, placing personal data and demographic data in their respective column families, with each column family having multiple files.

This lecture explains how hbase uses timestamps as versions within a cell, with a default limit per column family and queries to fetch latest or historical values by time.

Learn how Apache HBase operates on a Hadoop-based column-oriented NoSQL database, including starting daemons, the master and region servers, and using general commands to manage clusters, regions, and tables.

Explore data definition language (ddl) in hbase for defining database structures. Create tables with a table name and column family, then describe, list, check existence, and drop after disabling.

Learn to manage HBase tables using DDL commands to disable, enable, and verify status, including batch operations on tables by prefix.

Explore the core components of Apache HBase: the master server coordinates requests, region servers handle data subsets, and the admin and data interfaces manage schema and data operations.

Zookeeper coordinates the master and region servers in HBase, handling region management, heartbeats every 10 seconds, and failover to recover from region crashes.

Learn how a client interacts with an HBase cluster by using zookeeper to locate the master, access the plane to retrieve and update records, and understand the master-slave architecture.

Enable Bloom filters on column families in Apache HBase, leveraging a probabilistic data structure to test membership with space-efficient memory and faster lookups.

Explore the structure of HBase HFile, detailing edge files and store files built from blocks, their memory and write-ahead workflow, data segmentation, indexing, and optional compression.

Explore server-side filtering in HBase using custom filters with get and scan to return a subset of data, including column prefix and timestamp filters to reduce bandwidth.

Explore the FirstKeyOnlyFilter and PrefixFilter in HBase, demonstrating how the first value is returned per row and how a row prefix narrows results.

Explore column prefix filter and multiple column prefix filter in Apache HBase, including syntax and examples on airline table scans using column qualifiers like source and destination.

Learn how to apply column and page filters in HBase with ColumnCountGetFilter and PageFilter to limit columns and pages during table scans, with practical examples.

Learn how to use the value filter in HBase to return values that meet a comparison or prefix condition during a scan, specifying the table, column family, and qualifier.

Learn to integrate HBase with Pig tool part II by creating hdfs directories, copying local data to hdfs, and validating content with ls and cat.

Explore integrating HBase with Pig tool through hands-on steps to create HBase tables, define schema, load and verify data via MapReduce workflows, and query with scans.

Explore integrating Apache HBase with Apache Hive, building a Hive data warehouse, creating an employee database and table, loading a sample dataset, and validating data via select queries.

Create a table in Apache HBase using Java API by instantiating the configuration, building a table descriptor with column families, and invoking the createTable method to deploy an empty table.

Insert data into an hbase table with java by creating a configuration, instantiating the table, and using a put with a column family, qualifier, and value, then save and close.

Learn to scan an HBase table with the Java API by configuring, instantiating scan and table objects, selecting column families and qualifiers, and printing results.

List all tables in an HBase instance using the Java API, print their names from table descriptors, and enable a disabled table with enableTable after checking isTableEnabled.

Delete a table in Apache HBase using the Java API by disabling the table, parsing its name, calling the delete table method, and verifying deletion by listing tables.