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DBMS > Google Cloud Datastore vs. Kinetica vs. Microsoft Azure Table Storage vs. RDF4J vs. searchxml

System Properties Comparison Google Cloud Datastore vs. Kinetica vs. Microsoft Azure Table Storage vs. RDF4J vs. searchxml

Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonKinetica  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonsearchxml  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformFully vectorized database across both GPUs and CPUsA Wide Column Store for rapid development using massive semi-structured datasetsRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.DBMS for structured and unstructured content wrapped with an application server
Primary database modelDocument storeRelational DBMSWide column storeRDF storeNative XML DBMS
Search engine
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.13
Rank#71  Overall
#12  Document stores
Score0.42
Rank#261  Overall
#120  Relational DBMS
Score3.55
Rank#80  Overall
#6  Wide column stores
Score0.72
Rank#222  Overall
#9  RDF stores
Score0.00
Rank#385  Overall
#7  Native XML DBMS
#24  Search engines
Websitecloud.google.com/­datastorewww.kinetica.comazure.microsoft.com/­en-us/­services/­storage/­tablesrdf4j.orgwww.searchxml.net/­category/­products
Technical documentationcloud.google.com/­datastore/­docsdocs.kinetica.comrdf4j.org/­documentationwww.searchxml.net/­support/­handouts
DeveloperGoogleKineticaMicrosoftSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.informationpartners gmbh
Initial release20082012201220042015
Current release7.1, August 20211.0
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoEclipse Distribution License (EDL), v1.0.commercial
Cloud-based only infoOnly available as a cloud serviceyesnoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++JavaC++
Server operating systemshostedLinuxhostedLinux
OS X
Unix
Windows
Windows
Data schemeschema-freeyesschema-freeyes infoRDF Schemasschema-free
Typing infopredefined data types such as float or dateyes, details hereyesyesyesyes
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.nononoyes
Secondary indexesyesyesnoyesyes
SQL infoSupport of SQLSQL-like query language (GQL)SQL-like DML and DDL statementsnonono
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
RESTful HTTP API
RESTful HTTP APIJava API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
RESTful HTTP API
WebDAV
XQuery
XSLT
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Java
JavaScript (Node.js)
Python
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
PHP
Python
C++ infomost other programming languages supported via APIs
Server-side scripts infoStored proceduresusing Google App Engineuser defined functionsnoyesyes infoon the application server
TriggersCallbacks using the Google Apps Engineyes infotriggers when inserted values for one or more columns fall within a specified rangenoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud servicenonenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosSource-replica replicationyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.noneyes infosychronisation to multiple collections
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate 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.Immediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnooptimistic lockingACID infoIsolation support depends on the API usedmultiple readers, single writer
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoin-memory storage is supported as wellyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoGPU vRAM or System RAMnono
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users and roles on table levelAccess rights based on private key authentication or shared access signaturesnoDomain, group and role-based access control at the document level and for application services

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More resources
Google Cloud DatastoreKineticaMicrosoft Azure Table StorageRDF4J infoformerly known as Sesamesearchxml
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