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DBMS > Amazon Aurora vs. Spark SQL

System Properties Comparison Amazon Aurora vs. Spark SQL

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

Editorial information provided by DB-Engines
NameAmazon Aurora  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionMySQL and PostgreSQL compatible cloud service by AmazonSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRelational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score9.19
Rank#44  Overall
#27  Relational DBMS
Score18.63
Rank#34  Overall
#21  Relational DBMS
Websiteaws.amazon.com/­rds/­auroraspark.apache.org/­sql
Technical documentationdocs.aws.amazon.com/­AmazonRDS/­latest/­AuroraUserGuide/­CHAP_Aurora.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperAmazonApache Software Foundation
Initial release20152014
Current release3.0.1, October 2020
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageScala
Server operating systemshostedLinux
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
JDBC
ODBC
Supported programming languagesAda
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Java
Python
R
Scala
Server-side scripts infoStored proceduresyesno
Triggersyesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnone
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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More resources
Amazon AuroraSpark SQL
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