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DBMS > Bangdb vs. Firebase Realtime Database vs. Google Cloud Datastore vs. Hive

System Properties Comparison Bangdb vs. Firebase Realtime Database vs. Google Cloud Datastore vs. Hive

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Editorial information provided by DB-Engines
NameBangdb  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHive  Xexclude from comparison
DescriptionConverged and high performance database for device data, events, time series, document and graphCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platformdata warehouse software for querying and managing large distributed datasets, built on Hadoop
Primary database modelDocument store
Graph DBMS
Time Series DBMS
Document storeDocument storeRelational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.16
Rank#338  Overall
#47  Document stores
#32  Graph DBMS
#31  Time Series DBMS
Score13.64
Rank#39  Overall
#6  Document stores
Score4.36
Rank#72  Overall
#12  Document stores
Score59.76
Rank#18  Overall
#12  Relational DBMS
Websitebangdb.comfirebase.google.com/­products/­realtime-databasecloud.google.com/­datastorehive.apache.org
Technical documentationdocs.bangdb.comfirebase.google.com/­docs/­databasecloud.google.com/­datastore/­docscwiki.apache.org/­confluence/­display/­Hive/­Home
DeveloperSachin Sinha, BangDBGoogle infoacquired by Google 2014GoogleApache Software Foundation infoinitially developed by Facebook
Initial release2012201220082012
Current releaseBangDB 2.0, October 20213.1.3, April 2022
License infoCommercial or Open SourceOpen Source infoBSD 3commercialcommercialOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++Java
Server operating systemsLinuxhostedhostedAll OS with a Java VM
Data schemeschema-freeschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyes: string, long, double, int, geospatial, stream, eventsyesyes, details hereyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nonono
Secondary indexesyes infosecondary, composite, nested, reverse, geospatialyesyesyes
SQL infoSupport of SQLSQL like support with command line toolnoSQL-like query language (GQL)SQL-like DML and DDL statements
APIs and other access methodsProprietary protocol
RESTful HTTP API
Android
iOS
JavaScript API
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
Thrift
Supported programming languagesC
C#
C++
Java
Python
Java
JavaScript
Objective-C
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Java
PHP
Python
Server-side scripts infoStored proceduresnolimited functionality with using 'rules'using Google App Engineyes infouser defined functions and integration of map-reduce
Triggersyes, Notifications (with Streaming only)Callbacks are triggered when data changesCallbacks using the Google Apps Engineno
Partitioning methods infoMethods for storing different data on different nodesSharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithmShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor, Knob for CAP (enterprise version only)Multi-source replication using Paxosselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infousing Google Cloud Dataflowyes infoquery execution via MapReduce
Consistency concepts infoMethods to ensure consistency in a distributed systemTunable consistency, set CAP knob accordinglyEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Immediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Eventual Consistency
Foreign keys infoReferential integritynonoyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyesACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsno
Concurrency infoSupport for concurrent manipulation of datayes, optimistic concurrency controlyesyesyes
Durability infoSupport for making data persistentyes, implements WAL (Write ahead log) as wellyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes, run db with in-memory only modeno
User concepts infoAccess controlyes (enterprise version only)yes, based on authentication and database rulesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and roles

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More resources
BangdbFirebase Realtime DatabaseGoogle Cloud DatastoreHive
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