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DBMS > Kinetica vs. Microsoft Azure Data Explorer vs. RDFox

System Properties Comparison Kinetica vs. Microsoft Azure Data Explorer vs. RDFox

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonRDFox  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsFully managed big data interactive analytics platformHigh performance knowledge graph and semantic reasoning engine
Primary database modelRelational DBMSRelational DBMS infocolumn orientedGraph DBMS
RDF store
Secondary database modelsSpatial DBMS
Time Series DBMS
Document store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.23
Rank#308  Overall
#25  Graph DBMS
#14  RDF stores
Websitewww.kinetica.comazure.microsoft.com/­services/­data-explorerwww.oxfordsemantic.tech
Technical documentationdocs.kinetica.comdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.oxfordsemantic.tech
DeveloperKineticaMicrosoftOxford Semantic Technologies
Initial release201220192017
Current release7.1, August 2021cloud service with continuous releases6.0, Septermber 2022
License infoCommercial or Open Sourcecommercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++C++
Server operating systemsLinuxhostedLinux
macOS
Windows
Data schemeyesFixed schema with schema-less datatypes (dynamic)yes infoRDF schemas
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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.noyes
Secondary indexesyesall fields are automatically indexed
SQL infoSupport of SQLSQL-like DML and DDL statementsKusto Query Language (KQL), SQL subsetno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
RESTful HTTP API
SPARQL 1.1
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
Java
Server-side scripts infoStored proceduresuser defined functionsYes, possible languages: KQL, Python, R
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangeyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
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. Replication either local, cross-facility or geo-redundant.replication via a shared file system
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationEventual Consistency
Immediate Consistency
Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups
Foreign keys infoReferential integrityyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoGPU vRAM or System RAMnoyes
User concepts infoAccess controlAccess rights for users and roles on table levelAzure Active Directory AuthenticationRoles, resources, and access types

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More resources
KineticaMicrosoft Azure Data ExplorerRDFox
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