Difference between HBase and MongoDB Last Updated : 08 Jun, 2022 Comments Improve Suggest changes Like Article Like Report 1. HBase: This model is used to provide random access to a large amount of structured data. It builds on the top of the Hadoop file system and is column-oriented in nature. It is used to store the data in HDFS. It is an open-source database that provides data replication. Advantages: High availability because of no SPoF (Single Point of Failure)ScalabilityOperational SimplicityAdd-on applications like Apache Phoenix enables a SQL interface Application Areas: Internet of Things (IoT)fraud detection applicationsproduct catalogs and playlistsmessaging applicationsweb applications Users: Apple BloombergAirtel Thomson Reuters, and more. 2. MongoDB: MongoDB is document-oriented and does not need the row and column format of data. It gives high performance and is dynamic in nature where we don't need to predefine a schema like in conventional RDBMS. MongoDB stores data in JSON format which allows you to send the data in any form you want. It is a cross-platform database that works with almost every platform like Windows, Linux, etc. Advantages: Consistent Interface for whole development that simplifies the developmentMulti-cloud InfrastructureManaged for operational simplicity Application Areas: Internet of Things and Time Series eCommerce and Payment Processing Analytic and AI Gaming Mainframe Offload Users: AmadeusAstraZenecaBoschCiscoCERNForbesVerizon WirelessDifference between HBase and MongoDB: S. No.ParametersHBaseMongoDB1.Developed byDeveloped by Apache Software Foundation.Developed by MongoDB Inc.2.Websitehbase.apache.org www.mongodb.com 3.Technical Documentationhbase.apache.orgdocs.mongodb.com/manual4.Primary Database ModelIt is based on column-oriented.It is based on a document store.5.Implementation Language It is written in JAVA.It is written in C++.6.Server OSLinux, Unix, WindowsLinux, OS X, Solaris, Windows7.Supported Programming LanguagesC, C#, C++, Groovy, Java, PHP, Python, ScalaC, C#, C++, Erlang, Haskell, Java, JavaScript, Perl, PHP, Python, Ruby, Scala8.EditionCommunityCommunity (Free) and Enterprise9.Secondary IndexIt has no secondary indexes.It has secondary indexes.10.Storing dataData are stored in form of key/value pairs.Data are not stored in form of key/value pair.11.Data TypeHBase is used to store structured data.MongoDB is used to store any kind of data. Comment More infoAdvertise with us Next Article Difference between HBase and MongoDB B bansal_rtk_ Follow Improve Article Tags : DBMS Difference Between Similar Reads Difference between Hive and MongoDB 1. Hive : Hive is a data warehouse software for querying and managing large distributed datasets, built on Hadoop. It is developed by Apache Software Foundation in 2012. It contains two modules, one is MapReduce and another is Hadoop Distributed File System (HDFS). It stores schema in a database and 2 min read Difference between Firebase and MongoDB 1. Firebase : Firebase is developed by Google in 2012. It is a database to store and synchronize data in real-time. It is a Cloud-hosted real-time document store and gives the flexibility to access data from any device iOS, Android. JavaScript clients share one Realtime Database instance and automat 2 min read Difference Between Hadoop and MongoDB Hadoop and MongoDB are two important technologies in the area of big data processing and each with its unique strengths. Hadoop which is known for its scalability and ability to handle batch processing. MongoDB offers flexibility and performance for real-time data processing. In this article, We wil 6 min read Difference between RDBMS and HBase When we want to manage & store data, the Selection of the ideal database is very crucial, since different datatypes are suited for different types of data & workloads. Two major types of databases are present, they are RDBMS ( Relational Database Management System ) & HBase ( Hadoop Data 9 min read Difference between Impala and MongoDB 1. Impala : Impala is a query engine that runs on Hadoop. It is an open source software and massively parallel processing SQL query engine. It supports in-memory data processing. It is pioneering the use of the Parquet file format, a columnar storage layout that is optimized for large-scale queries 2 min read Like