difference between hbase and hdfs - Wikitechy

What is the difference between Hive and HBase ?

Editor — Tue, 13 Jul 2021 22:18:28 +0000

Hive	HBase
Hive is query engine	HBase is a data storage particularly for unstructured data.
Apache Hive is mainly used for batch processing i.e. OLAP	HBase is extensively used for transactional processing wherein the response time of the query is not highly interactive i.e. OLTP.
Operations in Hive are used to transformed into mapreduce jobs.	Operations in HBase are run in real-time on the database
For big data applications that require complex and fine grained processing, Hadoop MapReduce is the best choice.	HBase should be used when Data model schema is sparse.
It used for data warehousing requirements the programmers do not write complex mapreduce code.	HBase is an ideal big data solution if the application requires random read or random write operations or both.
Hive does not currently support update statements.	HBase queries are written in a custom language that needs to be learned.
Hive does not provide interactive querying it only runs batch processes on Hadoop.	Apache HBase is a NoSQL key/value store which runs on top of HDFS.
Hive has some limitations of high latency	HBase does not have analytical capabilities
Hive is to analytical queries.	HBase is to real-time querying
Hive used for analytical querying of data collected over a period of time.Hive should not be used for real-time querying.	HBase is perfect for real-time example Facebook use for messaging and real-time analytics. They may even be using it to count Facebook likes.

Editor — Tue, 13 Jul 2021 21:51:23 +0000

Hadoop is an open source software stack that runs on a cluster of machines.
Hadoop provides distributed storage and distributed process for very big data sets.

It has following 2 core components:

Hadoop Distributed file system or HDFS is a Java based distributed file system that enables us to store big data across multiple nodes in a Hadoop cluster.
So, if you install Hadoop, you will get HDFS as an underlying storage system for storing the big data sets in the distributed environment.

MapReduce is a programming framework for writing applications that method massive amounts of structured and unstructured data in parallel across a cluster of thousands of machines, in a reliable, fault-tolerant manner.

Apache HBase is Hadoop database, a distributed, scalable, column oriented big data store.
HBase is built on top of HDFS means that data you store in HBase is stored in HDFS itself.

Hive is an important tool for Hadoop ecosystem it provides an SQL for querying data in HDFS, other file systems that integrate with Hadoop like MapR-FS and Amazon’s S3 and databases like HBase(the Hadoop database) and Cassandra.
Hive too like HBase stores data into HDFS but it uses MapReduce too. It compiles queries into MapReduce jobs and runs them on the cluster. It was the primary abstraction engines to be built on top of MapReduce. Hive needs a metastore(JDBC compliant RDBMS) to store its metadata.

Editor — Mon, 12 Jul 2021 19:18:59 +0000

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 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.

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.

Editor — Mon, 12 Jul 2021 08:40:35 +0000

Pig	Hive	Hbase
It is used for semi structured data.	Hive is query engine	HBase is a data storage particularly for unstructured data.
Pig Hadoop Component is generally used by Researchers and Programmers.	Apache Hive is mainly used for batch processing i.e. OLAP and creating reports.	HBase is extensively used for transactional processing where in the response time of the query is not highly interactive i.e. OLTP
Pig Hadoop Component operates on the client side of any cluster.	Hive Hadoop Component operates on the server side of any cluster.	Operations in HBase are run in real-time on the database
Avro supported for Pig.	Hive does not support Avro.	The client which is reading/writing the data has to deal with the avro schemas, after HBase delivered the raw data to it.
Pig Hadoop is a great ETL tool for big data because of its powerful transformation and processing capabilities.	Hive Hadoop Component is helpful for ETL.	Hbase Component is helpful for ETL.
Pig are high-level languages that compile to MapReduce.	Hive is also a high-level languages that compile to MapReduce.	HBase allows Hadoop to support the transactions on key value pairs.
Pig is also SQL-like but varies to a great extent and thus it will take some time efforts to master Pig.	Hive directly leverages SQL expertise and thus can be learnt easily.	HBase allows you to do quick random versus scan all of data sequentially, do insert/update/delete from middle, and not just add/append.