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DBMS > Apache Spark (SQL) vs. EDB Postgres vs. Linter

System Properties Comparison Apache Spark (SQL) vs. EDB Postgres vs. Linter

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Editorial information provided by DB-Engines
NameApache Spark (SQL)  Xexclude from comparisonEDB Postgres  Xexclude from comparisonLinter  Xexclude from comparison
DescriptionApache Spark SQL is a component on top of 'Spark Core' for structured data processingThe EDB Postgres Platform is an enterprise-class data management platform based on the open source database PostgreSQL with flexible deployment options and Oracle compatibility features, complemented by tool kits for management, integration, and migration.RDBMS for high security requirements
Primary database modelRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score20.40
Rank#29  Overall
#18  Relational DBMS
Score1.64
Rank#133  Overall
#61  Relational DBMS
Score0.00
Rank#382  Overall
#155  Relational DBMS
Websitespark.apache.org/­sqlwww.enterprisedb.comlinter.ru
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.enterprisedb.com/­docs
DeveloperApache Software FoundationEnterpriseDBrelex.ru
Initial release201420051990
Current release3.5.0 ( 2.13), September 202314, December 2021
License infoCommercial or Open SourceOpen Source infoApache 2.0commercial infoBSD for PostgreSQL-componentscommercial
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaCC and C++
Server operating systemsLinux
OS X
Windows
Linux
Windows
AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesyesyes
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 functionality.no
Secondary indexesnoyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyes infostandard with numerous extensionsyes
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)
Supported programming languagesJava
Python
R
Scala
.Net
C
C++
Delphi
Java
Perl
PHP
Python
Tcl
C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl
Server-side scripts infoStored proceduresnouser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.yes infoproprietary syntax with the possibility to convert from PL/SQL
Triggersnoyesyes
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark Corehorizontal partitioning infoby hash, list or rangenone
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replicationSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlnofine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
Apache Spark (SQL)EDB PostgresLinter
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