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DBMS > Apache Spark (SQL) vs. Microsoft SQL Server

System Properties Comparison Apache Spark (SQL) vs. Microsoft SQL Server

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Editorial information provided by DB-Engines
NameApache Spark (SQL)  Xexclude from comparisonMicrosoft SQL Server  Xexclude from comparison
DescriptionApache Spark SQL is a component on top of 'Spark Core' for structured data processingMicrosofts flagship relational DBMS
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
Score21.62
Rank#29  Overall
#18  Relational DBMS
Score774.89
Rank#3  Overall
#3  Relational DBMS
Websitespark.apache.org/­sqlwww.microsoft.com/­en-us/­sql-server
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmllearn.microsoft.com/­en-US/­sql/­sql-server
DeveloperApache Software FoundationMicrosoft
Initial release20141989
Current release3.5.0 ( 2.13), September 2023SQL Server 2022, November 2022
License infoCommercial or Open SourceOpen Source infoApache 2.0commercial inforestricted free version is available
Cloud-based only infoOnly available as a cloud servicenono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaC++
Server operating systemsLinux
OS X
Windows
Linux
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.noyes
Secondary indexesnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyes
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)
Supported programming languagesJava
Python
R
Scala
C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic
Server-side scripts infoStored proceduresnoTransact SQL, .NET languages, R, Python and (with SQL Server 2019) Java
Triggersnoyes
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark Coretables can be distributed across several files (horizontal partitioning); sharding through federation
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyes, but depending on the SQL-Server Edition
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID
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.noyes
User concepts infoAccess controlnofine grained access rights according to SQL-standard

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More resources
Apache Spark (SQL)Microsoft SQL Server
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