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DBMS > Kinetica vs. Microsoft Azure SQL Database vs. Solr vs. Spark SQL vs. WakandaDB

System Properties Comparison Kinetica vs. Microsoft Azure SQL Database vs. Solr vs. Spark SQL vs. WakandaDB

Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonMicrosoft Azure SQL Database infoformerly SQL Azure  Xexclude from comparisonSolr  Xexclude from comparisonSpark SQL  Xexclude from comparisonWakandaDB  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsDatabase as a Service offering with high compatibility to Microsoft SQL ServerA widely used distributed, scalable search engine based on Apache LuceneSpark SQL is a component on top of 'Spark Core' for structured data processingWakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data
Primary database modelRelational DBMSRelational DBMSSearch engineRelational DBMSObject oriented DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Document store
Graph DBMS
Spatial DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score77.99
Rank#16  Overall
#11  Relational DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.03
Rank#364  Overall
#17  Object oriented DBMS
Websitewww.kinetica.comazure.microsoft.com/­en-us/­products/­azure-sql/­databasesolr.apache.orgspark.apache.org/­sqlwakanda.github.io
Technical documentationdocs.kinetica.comdocs.microsoft.com/­en-us/­azure/­azure-sqlsolr.apache.org/­resources.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwakanda.github.io/­doc
DeveloperKineticaMicrosoftApache Software FoundationApache Software FoundationWakanda SAS
Initial release20122010200620142012
Current release7.1, August 2021V129.6.0, April 20243.5.0 ( 2.13), September 20232.7.0 (April 29, 2019), April 2019
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache Version 2Open Source infoApache 2.0Open Source infoAGPLv3, extended commercial license available
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++C++JavaScalaC++, JavaScript
Server operating systemsLinuxhostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
OS X
Windows
Linux
OS X
Windows
Data schemeyesyesyes infoDynamic Fields enables on-the-fly addition of new fieldsyesyes
Typing infopredefined data types such as float or dateyesyesyes infosupports customizable data types and automatic typingyesyes
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.noyesyesnono
Secondary indexesyesyesyes infoAll search fields are automatically indexedno
SQL infoSupport of SQLSQL-like DML and DDL statementsyesSolr Parallel SQL InterfaceSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
ODBC
Java API
RESTful HTTP/JSON API
JDBC
ODBC
RESTful HTTP API
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Java
Python
R
Scala
JavaScript
Server-side scripts infoStored proceduresuser defined functionsTransact SQLJava pluginsnoyes
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangeyesyes infoUser configurable commands triggered on index changesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes, with always 3 replicas availableyesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDoptimistic lockingnoACID
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.yes infoGPU vRAM or System RAMyesnono
User concepts infoAccess controlAccess rights for users and roles on table levelfine grained access rights according to SQL-standardyesnoyes

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More resources
KineticaMicrosoft Azure SQL Database infoformerly SQL AzureSolrSpark SQLWakandaDB
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