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DBMS > HEAVY.AI vs. jBASE vs. Kinetica vs. MarkLogic vs. Microsoft Azure Data Explorer

System Properties Comparison HEAVY.AI vs. jBASE vs. Kinetica vs. MarkLogic vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonjBASE  Xexclude from comparisonKinetica  Xexclude from comparisonMarkLogic  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareA robust multi-value DBMS comprising development tools and middlewareFully vectorized database across both GPUs and CPUsOperational and transactional Enterprise NoSQL databaseFully managed big data interactive analytics platform
Primary database modelRelational DBMSMultivalue DBMSRelational DBMSDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMS infocolumn oriented
Secondary database modelsSpatial DBMSSpatial 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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score1.41
Rank#159  Overall
#3  Multivalue DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score5.92
Rank#58  Overall
#10  Document stores
#1  Native XML DBMS
#1  RDF stores
#6  Search engines
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websitegithub.com/­heavyai/­heavydb
www.heavy.ai
www.rocketsoftware.com/­products/­rocket-multivalue-application-development-platform/­rocket-jbasewww.kinetica.comwww.marklogic.comazure.microsoft.com/­services/­data-explorer
Technical documentationdocs.heavy.aidocs.rocketsoftware.com/­bundle?labelkey=jbase_5.9docs.kinetica.comdocs.marklogic.comdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperHEAVY.AI, Inc.Rocket Software (formerly Zumasys)KineticaMarkLogic Corp.Microsoft
Initial release20161991201220012019
Current release5.10, January 20225.77.1, August 202111.0, December 2022cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2; enterprise edition availablecommercialcommercialcommercial inforestricted free version is availablecommercial
Cloud-based only infoOnly available as a cloud servicenonononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++ and CUDAC, C++C++
Server operating systemsLinuxAIX
Linux
Windows
LinuxLinux
OS X
Windows
hosted
Data schemeyesschema-freeyesschema-free infoSchema can be enforcedFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesoptionalyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types
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.noyesnoyesyes
Secondary indexesnoyesyesall fields are automatically indexed
SQL infoSupport of SQLyesEmbedded SQL for jBASE in BASICSQL-like DML and DDL statementsyes infoSQL92Kusto Query Language (KQL), SQL subset
APIs and other access methodsJDBC
ODBC
Thrift
Vega
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API
JDBC
ODBC
RESTful HTTP API
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBC/Thrift
Python
.Net
Basic
Jabbascript
Java
C++
Java
JavaScript (Node.js)
Python
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresnoyesuser defined functionsyes infovia XQuery or JavaScriptYes, possible languages: KQL, Python, R
Triggersnoyesyes infotriggers when inserted values for one or more columns fall within a specified rangeyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesSharding infoRound robinShardingShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationyesSource-replica replicationyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynonoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACID infocan act as a resource manager in an XA/JTA transactionno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes infoGPU vRAM or System RAMyes, with Range Indexesno
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights can be defined down to the item levelAccess rights for users and roles on table levelRole-based access control at the document and subdocument levelsAzure Active Directory Authentication

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More resources
HEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022jBASEKineticaMarkLogicMicrosoft Azure Data Explorer
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