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DBMS > Cachelot.io vs. Microsoft SQL Server vs. Netezza vs. PostgreSQL vs. Spark SQL

System Properties Comparison Cachelot.io vs. Microsoft SQL Server vs. Netezza vs. PostgreSQL vs. Spark SQL

Editorial information provided by DB-Engines
NameCachelot.io  Xexclude from comparisonMicrosoft SQL Server  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonPostgreSQL  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionIn-memory caching systemMicrosofts flagship relational DBMSData warehouse and analytics appliance part of IBM PureSystemsWidely used open source RDBMS infoDeveloped as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQLSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelKey-value storeRelational DBMSRelational DBMSRelational DBMS infowith object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.Relational DBMS
Secondary database modelsDocument store
Graph DBMS
Spatial DBMS
Document store
Graph DBMS infowith Apache Age
Spatial DBMS
Vector DBMS infowith pgvector extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.00
Rank#383  Overall
#60  Key-value stores
Score824.29
Rank#3  Overall
#3  Relational DBMS
Score9.06
Rank#46  Overall
#29  Relational DBMS
Score645.54
Rank#4  Overall
#4  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitecachelot.iowww.microsoft.com/­en-us/­sql-serverwww.ibm.com/­products/­netezzawww.postgresql.orgspark.apache.org/­sql
Technical documentationlearn.microsoft.com/­en-US/­sql/­sql-serverwww.postgresql.org/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperMicrosoftIBMPostgreSQL Global Development Group infowww.postgresql.org/­developerApache Software Foundation
Initial release2015198920001989 info1989: Postgres, 1996: PostgreSQL2014
Current releaseSQL Server 2022, November 202216.3, May 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoSimplified BSD Licensecommercial inforestricted free version is availablecommercialOpen Source infoBSDOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++CScala
Server operating systemsFreeBSD
Linux
OS X
Linux
Windows
Linux infoincluded in applianceFreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows
Linux
OS X
Windows
Data schemeschema-freeyesyesyesyes
Typing infopredefined data types such as float or datenoyesyesyesyes
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.noyesyes infospecific XML-type available, but no XML query functionality.no
Secondary indexesnoyesyesyesno
SQL infoSupport of SQLnoyesyesyes infostandard with numerous extensionsSQL-like DML and DDL statements
APIs and other access methodsMemcached protocolADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)
JDBC
ODBC
OLE DB
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
Supported programming languages.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby
C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic
C
C++
Fortran
Java
Lua
Perl
Python
R
.Net
C
C++
Delphi
Java infoJDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoTransact SQL, .NET languages, R, Python and (with SQL Server 2019) Javayesuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.no
Triggersnoyesnoyesno
Partitioning methods infoMethods for storing different data on different nodesnonetables can be distributed across several files (horizontal partitioning); sharding through federationShardingpartitioning by range, list and (since PostgreSQL 11) by hashyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyes, but depending on the SQL-Server EditionSource-replica replicationSource-replica replication infoother methods possible by using 3rd party extensionsnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentnoyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnono
User concepts infoAccess controlnofine grained access rights according to SQL-standardUsers with fine-grained authorization conceptfine grained access rights according to SQL-standardno

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More resources
Cachelot.ioMicrosoft SQL ServerNetezza infoAlso called PureData System for Analytics by IBMPostgreSQLSpark SQL
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