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DBMS > Microsoft SQL Server vs. Spark SQL

System Properties Comparison Microsoft SQL Server vs. Spark SQL

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Our visitors often compare Microsoft SQL Server and Spark SQL with PostgreSQL, Snowflake and MySQL.

Editorial information provided by DB-Engines
NameMicrosoft SQL Server  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionMicrosofts flagship relational DBMSSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRelational DBMS
Secondary database modelsDocument store
Graph DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score912.51
Rank#3  Overall
#3  Relational DBMS
Score21.90
Rank#36  Overall
#22  Relational DBMS
Websitewww.microsoft.com/­en-us/­sql-serverspark.apache.org/­sql
Technical documentationdocs.microsoft.com/­en-US/­sql/­sql-serverspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperMicrosoftApache Software Foundation
Initial release19892014
Current releaseSQL Server 2019, November 20193.3.0 ( 2.13), June 2022
License infoCommercial or Open Sourcecommercial inforestricted free version is availableOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Scala
Server operating systemsLinux
Windows
Linux
OS X
Windows
Data schemeyesyes
Typing infopredefined data types such as float or dateyesyes
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.yesno
Secondary indexesyesno
SQL infoSupport of SQLyesSQL-like DML and DDL statements
APIs and other access methodsADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)
JDBC
ODBC
Supported programming languagesC#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic
Java
Python
R
Scala
Server-side scripts infoStored proceduresTransact SQL, .NET languages, R, Python and (with SQL Server 2019) Javano
Triggersyesno
Partitioning methods infoMethods for storing different data on different nodestables can be distributed across several files (horizontal partitioning); sharding through federationyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyes, but depending on the SQL-Server Editionnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integrityyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesno
User concepts infoAccess controlfine grained access rights according to SQL-standardno

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3rd partiesSQL Complete: An advanced IntelliSense-style code completion add-in for SSMS and Visual Studio. Write, beautify, refactor your SQL code and give your productivity a dramatic boost.
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SQLFlow: Provides a visual representation of the overall flow of data. Automated SQL data lineage analysis across Databases, ETL, Business Intelligence, Cloud and Hadoop environments by parsing SQL Script and stored procedure.
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More resources
Microsoft SQL ServerSpark SQL
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