Prerequisite – Introduction to Hadoop
HBase is a data model that is similar to Google’s big table. It is an open source, distributed database developed by Apache software foundation written in Java. HBase is an essential part of our Hadoop ecosystem. HBase runs on top of HDFS (Hadoop Distributed File System). It can store massive amounts of data from terabytes to petabytes. It is column oriented and horizontally scalable.
Features of HBase –
- It is linearly scalable across various nodes as well as modularly scalable, as it divided across various nodes.
- HBase provides consistent read and writes.
- It provides atomic read and write means during one read or write process, all other processes are prevented from performing any read or write operations.
- It provides easy to use Java API for client access.
- It supports Thrift and REST API for non-Java front ends which supports XML, Protobuf and binary data encoding options.
- It supports a Block Cache and Bloom Filters for real-time queries and for high volume query optimization.
- HBase provides automatic failure support between Region Servers.
- It support for exporting metrics with the Hadoop metrics subsystem to files.
- It doesn’t enforce relationship within your data.
- It is a platform for storing and retrieving data with random access.
Facebook Messenger Platform was using Apache Casandra but it shifted from Apache Cassandra to HBase in November 2010. Facebook was trying to build a scalable and robust infrastructure to handle set of services like messages, email, chat and SMS into a real time conversation so that’s why HBase is best suited for that.
RDBMS Vs HBase –
- RDBMS is mostly Row Oriented whereas HBase is Column Oriented.
- RDBMS has fixed schema but in HBase we can scale or add columns in run time also.
- RDBMS is good for structured data whereas HBase is good for semi-structured data.
- RDBMS is optimized for joins but HBase is not optimized for joins.
- Difference between RDBMS and HBase
- Difference between Hive and HBase
- Introduction to Apache Cassandra
- Apache Cassandra (NOSQL database)
- Architecture of Apache Cassandra
- Concept of indexing in Apache Cassandra
- Collection Data Type in Apache Cassandra
- Pre-defined data type in Apache Cassandra
- Bulk Reading in Cassandra
- Data Mining: Data Attributes and Quality
- Arranging clustering column in descending order in Cassandra
- Snitches in Cassandra
- Traditional Data Mining Life Cycle (Crisp Methodology)
- Data Mining: Data Warehouse Process
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