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DBMS > Datomic vs. HEAVY.AI vs. Kinetica vs. Microsoft Azure Data Explorer vs. Solr

System Properties Comparison Datomic vs. HEAVY.AI vs. Kinetica vs. Microsoft Azure Data Explorer vs. Solr

Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonKinetica  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareFully vectorized database across both GPUs and CPUsFully managed big data interactive analytics platformA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSRelational DBMSRelational DBMSRelational DBMS infocolumn orientedSearch engine
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
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Websitewww.datomic.comgithub.com/­heavyai/­heavydb
www.heavy.ai
www.kinetica.comazure.microsoft.com/­services/­data-explorersolr.apache.org
Technical documentationdocs.datomic.comdocs.heavy.aidocs.kinetica.comdocs.microsoft.com/­en-us/­azure/­data-explorersolr.apache.org/­resources.html
DeveloperCognitectHEAVY.AI, Inc.KineticaMicrosoftApache Software Foundation
Initial release20122016201220192006
Current release1.0.6735, June 20235.10, January 20227.1, August 2021cloud service with continuous releases9.6.0, April 2024
License infoCommercial or Open Sourcecommercial infolimited edition freeOpen Source infoApache Version 2; enterprise edition availablecommercialcommercialOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ClojureC++ and CUDAC, C++Java
Server operating systemsAll OS with a Java VMLinuxLinuxhostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesyesyesFixed schema with schema-less datatypes (dynamic)yes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes infosupports customizable data types and automatic typing
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.nononoyesyes
Secondary indexesyesnoyesall fields are automatically indexedyes infoAll search fields are automatically indexed
SQL infoSupport of SQLnoyesSQL-like DML and DDL statementsKusto Query Language (KQL), SQL subsetSolr Parallel SQL Interface
APIs and other access methodsRESTful HTTP APIJDBC
ODBC
Thrift
Vega
JDBC
ODBC
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Java API
RESTful HTTP/JSON API
Supported programming languagesClojure
Java
All languages supporting JDBC/ODBC/Thrift
Python
C++
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresyes infoTransaction Functionsnouser defined functionsYes, possible languages: KQL, Python, RJava plugins
TriggersBy using transaction functionsnoyes infotriggers when inserted values for one or more columns fall within a specified rangeyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersSharding infoRound robinShardingSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersMulti-source replicationSource-replica replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationEventual Consistency
Immediate Consistency
Eventual Consistency
Foreign keys infoReferential integritynonoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnononooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes inforecommended only for testing and developmentyesyes infoGPU vRAM or System RAMnoyes
User concepts infoAccess controlnofine grained access rights according to SQL-standardAccess rights for users and roles on table levelAzure Active Directory Authenticationyes

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More resources
DatomicHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022KineticaMicrosoft Azure Data ExplorerSolr
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