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Difference between Hive and HBase ?

Hive

  • Hive is a datawarehousing package built on the top of Hadoop. It is mainly used for data analysis. It generally target towards users already comfortable with Structured Query Language (SQL).
  • It is similar to SQL and called Hive Query Language (HQL).
  • Hive manages and queries structured data. Moreover, hive abstracts complexity of Hadoop. It does not support
    • Not a full database.
    • Not a real time processing system.
    • Not SQL-92 compliant.
    • Does not provide row level insert, updates or deletes.
    • Doesn’t support transactions and limited sub-query support.
    • Query optimization in evolving stage.

Hbase

  • HBase is a column-oriented database management system that runs on top of Hadoop Distributed File System (HDFS).
  • It is well suited for sparse data sets, which are common in many Big Data use cases.
  • It is an opensource, distributed database developed by Apache software foundations.
  • Initially, it was named Google Big Table, afterwards it was re-named as HBase and is primarily written in Java.
  • It can store massive amount of data from terabytes to petabytes.
  • It is built for low-latency operations and is used extensively for read and write operations.
  • It stores large amount of data in the form of tables.
Difference between HIVE and HBASE
HIVE HBASE
Hive is a query engine. Data storage particularly for unstructured data.
Mainly used for batch processing. Extensively used for transactional processing.
Not a real time processing. Real-time processing.
Only for analytical queries. Real-time querying.
Runs on the top of Hadoop. Runs on the top of HDFS (Hadoop distributed file system).
Apache Hive is not a database. It support NoSQL database.
It has schema model. It is free from schema model.
Made for high latency operations. Made for low level latency operations.
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Difference between pig vs hive vs sql

Pig Hive Sql
Apache Pig also allows developers
to follow multiple query approach,
which reduces the data scan
iterations.		 Hive statements are similar to
Structured Query Language (SQL)
and limitations of Hive Query
Language(HQL) in terms of
the commands that it
understands, it is still very useful.		 Structured Query Language has been a programmer’s companion for decades. It was the real solution for extractingdata for further processing.
PigLatin is procedural to a
large extent.		 Hive is built with an analytical focus. Structured Query Language is Informative.
Pig requires that programmers
to learn something on top of
structured query language(SQL).		 Hive provides an open source
implementation of MapReduce.		 Structured Query Language demand of storing data
in a strict relational database
schemas and its declarative nature often deflects
focus from the ultimate purpose to extract data for analysis.
It offers a number of advantages
in terms of declaring execution plans
Extract, transform, load(ETL)
routines and pipeline modification.		 Hive lacks update and delete
functions but is superfast in reading
and processing huge volumes of data
faster than SQL.		 The DBMS systems that SQL operates on,
are considered to be faster than MapReduce.
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