hive vs hbase vs impala - Wikitechy

What is the Relationship Between Hadoop Hbase and Hive ?

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 — Tue, 13 Jul 2021 05:25:21 +0000

It is a Big Data framework which uses Hadoop Distributed File System to Store the data and MapReduce framework to process that data. Java is used as native language to write MapReduce programs.
Apache Hbase and Cassandra are both NoSQL databases.
It does not maintain the strict ACID transactions and require better data modelling to be used effectual.
Their presentation can be calculated by benchmarking the database using a tool called YCSB(Yahoo Cloud Serving Benchmark).
Factors examine in benchmarking can be read, write ,read-modify-write latency in executing queries.

Hbase:

It is a master less and shared-nothing, ring based architecture which does not depend upon Hadoop framework.
It is good for Write heavy and less read applications.

It is a collective processing framework to process the data using a language called Hive Query Language(HQL).
HQL is a sql wrapper on top of HDFS which protect writing Mapreduce programs in Java.
Instead one can use SQL like language to do their daily tasks.
Apache Hive is mainly used for ETL and data warehousing feature in Hadoop.
The process of hive is to create tables, joins, unions, aggregates etc. It develop visualization reports can be done by Tableau.

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.