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Articles Home » Big Data » HBase Tutorials for Beginners

HBase Tutorials for Beginners

Hive is an open source data warehouse which is initially developed by Facebook for analysis and querying datasets but is now under Apache software foundation.

Hive is developed on top of Hadoop as its data warehouse framework for querying and analysis of data is stored in HDFS.

Hive is useful for performing operations like data encapsulation, ad-hoc queries, & analysis of huge datasets. Hive's design reflects its targeted use as a system for managing and querying structured data.

HBase Vs Hive

Features	HBase	Hive
Data base model	Wide Column store	Relational DBMS
Data Schema	Schema- free	With Schema
SQL Support	No	Yes it uses HQL(Hive query language)
Partition methods	Sharding	Sharding
Consistency Level	Immediate Consistency	Eventual Consistency
Secondary indexes	No	Yes
Replication Methods	Selectable replication factor	Selectable replication factor

HBase VS RDBMS

While comparing HBase with Traditional Relational databases, we have to take three key areas into consideration. Those are data model, data storage, and data diversity.

HBASE	RDBMS
Schema-less in database	Having fixed schema in database
Column-oriented databases	Row oriented data store
Designed to store De-normalized data	Designed to store Normalized data
Wide and sparsely populated tables present in HBase	Contains thin tables in database
Supports automatic partitioning	Has no built in support for partitioning
Well suited for OLAP systems	Well suited for OLTP systems
Read only relevant data from database	Retrieve one row at a time and hence could read unnecessary data if only some of the data in a row is required
Structured and semi-structure data can be stored and processed using HBase	Structured data can be stored and processed using RDBMS
Enables aggregation over many rows and columns	Aggregation is an expensive operation

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