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DBMS > Kinetica vs. Microsoft Azure AI Search

System Properties Comparison Kinetica vs. Microsoft Azure AI Search

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Our visitors often compare Kinetica and Microsoft Azure AI Search with HEAVY.AI, Snowflake and Neo4j.

Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsSearch-as-a-service for web and mobile app development
Primary database modelRelational DBMSSearch engine
Secondary database modelsSpatial DBMS
Time Series DBMS
Vector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.69
Rank#234  Overall
#107  Relational DBMS
Score5.71
Rank#64  Overall
#8  Search engines
Websitewww.kinetica.comazure.microsoft.com/­en-us/­services/­search
Technical documentationdocs.kinetica.comlearn.microsoft.com/­en-us/­azure/­search
DeveloperKineticaMicrosoft
Initial release20122015
Current release7.1, August 2021V1
License infoCommercial or Open Sourcecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++
Server operating systemsLinuxhosted
Data schemeyesyes
Typing infopredefined data types such as float or dateyesyes
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.nono
Secondary indexesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
RESTful HTTP API
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
C#
Java
JavaScript
Python
Server-side scripts infoStored proceduresuser defined functionsno
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes infoImplicit feature of the cloud service
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integrityyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoGPU vRAM or System RAMno
User concepts infoAccess controlAccess rights for users and roles on table levelyes infousing Azure authentication

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More resources
KineticaMicrosoft Azure AI Search
Recent citations in the news

Kinetica Elevates RAG with Fast Access to Real-Time Data
26 March 2024, Datanami

Kinetica Delivers Real-Time Vector Similarity Search
21 March 2024, insideBIGDATA

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18 March 2024, SiliconANGLE News

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18 March 2024, GlobeNewswire

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30 October 2023, InfoWorld

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5 April 2024, InfoWorld

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