hive vs hbase performance - Wikitechy https://www.wikitechy.com/interview-questions/tag/hive-vs-hbase-performance/ Interview Questions Tue, 14 Sep 2021 12:14:12 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 https://www.wikitechy.com/interview-questions/wp-content/uploads/2025/10/cropped-wikitechy-icon-32x32.png hive vs hbase performance - Wikitechy https://www.wikitechy.com/interview-questions/tag/hive-vs-hbase-performance/ 32 32 What is the Relationship Between Hadoop Hbase and Hive ? https://www.wikitechy.com/interview-questions/hive/what-is-the-relationship-between-hadoop-hbase-and-hive/ https://www.wikitechy.com/interview-questions/hive/what-is-the-relationship-between-hadoop-hbase-and-hive/#respond Tue, 13 Jul 2021 21:51:23 +0000 https://www.wikitechy.com/interview-questions/?p=581

Hadoop HBase And Hive

hadoop hbase hive
  • 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:

HDFS :

  • 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:

  • 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.

HBase:

  • 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:

  • 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.
]]> https://www.wikitechy.com/interview-questions/hive/what-is-the-relationship-between-hadoop-hbase-and-hive/feed/ 0 What is the relationship between Apache Hadoop, HBase, Hive and Cassandra ? https://www.wikitechy.com/interview-questions/cassandra/what-is-the-relationship-between-apache-hadoop-hbase-hive-and-cassandra/ https://www.wikitechy.com/interview-questions/cassandra/what-is-the-relationship-between-apache-hadoop-hbase-hive-and-cassandra/#respond Tue, 13 Jul 2021 05:25:21 +0000 https://www.wikitechy.com/interview-questions/?p=436

Apache Hadoop:

  • 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 -Slave NoSQL database dependent upon Hadoop cluster .
  • It is good for Heavy reads and less Write applications
apache hadoop ecosystem

Apache Cassandra:

  • 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.

Apache Hive:

  • 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.
]]> https://www.wikitechy.com/interview-questions/cassandra/what-is-the-relationship-between-apache-hadoop-hbase-hive-and-cassandra/feed/ 0 What is the difference between Pig, Hive and HBase ? https://www.wikitechy.com/interview-questions/apache-pig/what-is-the-difference-between-pig-hive-and-hbase/ https://www.wikitechy.com/interview-questions/apache-pig/what-is-the-difference-between-pig-hive-and-hbase/#respond Mon, 12 Jul 2021 08:40:35 +0000 https://www.wikitechy.com/interview-questions/?p=211

Difference between Pig, Hive and HBase

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.
]]> https://www.wikitechy.com/interview-questions/apache-pig/what-is-the-difference-between-pig-hive-and-hbase/feed/ 0 What is Hbase used for, and what are the benefits that it provides over Pig and Hive ? https://www.wikitechy.com/interview-questions/apache-pig/what-is-hbase-used-for-and-what-are-the-benefits-that-it-provides-over-pig-and-hive/ https://www.wikitechy.com/interview-questions/apache-pig/what-is-hbase-used-for-and-what-are-the-benefits-that-it-provides-over-pig-and-hive/#respond Mon, 12 Jul 2021 07:58:18 +0000 https://www.wikitechy.com/interview-questions/?p=194

Hbase uses

  • HBASE will not replace Map Reduce. It is scalable distributed database.
  • Hbase processing data will act as Map Reduce.
  • The Hadoop database is define Apache HBase.
  • HBase is a sub-project of the Apache Hadoop project and is used to provide real-time read and write access to big data.
  • Hbase is a Bigtable clone.
  • Hbase is a open source.
  • Hbase called as Cascading connector.

what are the Features that it provides over Pig and Hive?

  • Possible to create tables in Hive and store data,it can even map the user’s existing HBase tables to Hive and operate on them.
  • Hive should be used for analyzing query of data collected over a period of time.
  • Hbase has got its own set of very good Application Programming Interface(API) which could be used to push/pull the data.
  • It has a good community for the future.It developed on top if hadoop already used to integration for the Hadoop platform.
  • Hbase can be integrated with MapReduce that can do bulk operation, like indexing, analytics etc.
    • Distributed storage
    • Table-like in data structure
    • multi-dimensional map
    • High scalability
    • High availability
    • High performance
  • A distributed data store that can scale horizontally to 1,000s of commodity servers and petabytes of indexed storage.
  • Designed to operate on top of the Hadoop distributed file system (HDFS) or Kosmos File System (KFS, aka Cloudstore) for scalability, fault tolerance, and high availability.
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