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DBMS > Amazon Redshift vs. Citus vs. Kinetica vs. Microsoft Azure Table Storage vs. Spark SQL

System Properties Comparison Amazon Redshift vs. Citus vs. Kinetica vs. Microsoft Azure Table Storage vs. Spark SQL

Editorial information provided by DB-Engines
NameAmazon Redshift  Xexclude from comparisonCitus  Xexclude from comparisonKinetica  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionLarge scale data warehouse service for use with business intelligence toolsScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLFully vectorized database across both GPUs and CPUsA Wide Column Store for rapid development using massive semi-structured datasetsSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRelational DBMSRelational DBMSWide column storeRelational DBMS
Secondary database modelsDocument storeSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score17.94
Rank#34  Overall
#21  Relational DBMS
Score2.21
Rank#118  Overall
#56  Relational DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score4.48
Rank#75  Overall
#6  Wide column stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websiteaws.amazon.com/­redshiftwww.citusdata.comwww.kinetica.comazure.microsoft.com/­en-us/­services/­storage/­tablesspark.apache.org/­sql
Technical documentationdocs.aws.amazon.com/­redshiftdocs.citusdata.comdocs.kinetica.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperAmazon (based on PostgreSQL)KineticaMicrosoftApache Software Foundation
Initial release20122010201220122014
Current release8.1, December 20187.1, August 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoAGPL, commercial license also availablecommercialcommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCCC, C++Scala
Server operating systemshostedLinuxLinuxhostedLinux
OS X
Windows
Data schemeyesyesyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesyesyes
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 infospecific XML type available, but no XML query functionalitynonono
Secondary indexesrestrictedyesyesnono
SQL infoSupport of SQLyes infodoes not fully support an SQL-standardyes infostandard, with numerous extensionsSQL-like DML and DDL statementsnoSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
RESTful HTTP API
RESTful HTTP APIJDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C++
Java
JavaScript (Node.js)
Python
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions infoin Pythonuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.user defined functionsnono
Triggersnoyesyes infotriggers when inserted values for one or more columns fall within a specified rangenono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding infoImplicit feature of the cloud serviceyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyesSource-replica replication infoother methods possible by using 3rd party extensionsSource-replica replicationyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integrityyes infoinformational only, not enforced by the systemyesyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnooptimistic lockingno
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.yesnoyes infoGPU vRAM or System RAMnono
User concepts infoAccess controlfine grained access rights according to SQL-standardfine grained access rights according to SQL-standardAccess rights for users and roles on table levelAccess rights based on private key authentication or shared access signaturesno

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More resources
Amazon RedshiftCitusKineticaMicrosoft Azure Table StorageSpark SQL
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