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DBMS > AnzoGraph DB vs. Firebase Realtime Database vs. GridDB vs. Valentina Server

System Properties Comparison AnzoGraph DB vs. Firebase Realtime Database vs. GridDB vs. Valentina Server

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Editorial information provided by DB-Engines
NameAnzoGraph DB  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonGridDB  Xexclude from comparisonValentina Server  Xexclude from comparison
DescriptionScalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualizationCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.Scalable in-memory time series database optimized for IoT and Big DataObject-relational database and reports server
Primary database modelGraph DBMS
RDF store
Document storeTime Series DBMSRelational DBMS
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.23
Rank#307  Overall
#24  Graph DBMS
#13  RDF stores
Score14.29
Rank#39  Overall
#6  Document stores
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score0.17
Rank#327  Overall
#145  Relational DBMS
Websitecambridgesemantics.com/­anzographfirebase.google.com/­products/­realtime-databasegriddb.netwww.valentina-db.net
Technical documentationdocs.cambridgesemantics.com/­anzograph/­userdoc/­home.htmfirebase.google.com/­docs/­databasedocs.griddb.netvalentina-db.com/­docs/­dokuwiki/­v5/­doku.php
DeveloperCambridge SemanticsGoogle infoacquired by Google 2014Toshiba CorporationParadigma Software
Initial release2018201220131999
Current release2.3, January 20215.1, August 20225.7.5
License infoCommercial or Open Sourcecommercial infofree trial version availablecommercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercial
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++
Server operating systemsLinuxhostedLinuxLinux
OS X
Windows
Data schemeSchema-free and OWL/RDFS-schema supportschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampyes
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 indexesnoyesyesyes
SQL infoSupport of SQLSPARQL and SPARQL* as primary query language. Cypher preview.noSQL92, SQL-like TQL (Toshiba Query Language)yes
APIs and other access methodsApache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL
Android
iOS
JavaScript API
RESTful HTTP API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
ODBC
Supported programming languagesC++
Java
Python
Java
JavaScript
Objective-C
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET
Server-side scripts infoStored proceduresuser defined functions and aggregateslimited functionality with using 'rules'noyes
TriggersnoCallbacks are triggered when data changesyesyes
Partitioning methods infoMethods for storing different data on different nodesAutomatic shardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication in MPP-ClusterSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsKerberos/HDFS data loadingnoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency in MPP-ClusterEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Immediate consistency within container, eventual consistency across containers
Foreign keys infoReferential integrityno infonot needed in graphsnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyesACID at container level
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes
User concepts infoAccess controlAccess rights for users and rolesyes, based on authentication and database rulesAccess rights for users can be defined per databasefine grained access rights according to SQL-standard
More information provided by the system vendor
AnzoGraph DBFirebase Realtime DatabaseGridDBValentina Server
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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AnzoGraph DBFirebase Realtime DatabaseGridDBValentina Server
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