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DBMS > Databricks vs. EDB Postgres vs. Spark SQL

System Properties Comparison Databricks vs. EDB Postgres vs. Spark SQL

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonEDB Postgres  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.The 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.Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument store
Relational DBMS
Relational DBMSRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score74.34
Rank#17  Overall
#3  Document stores
#11  Relational DBMS
Score2.46
Rank#121  Overall
#58  Relational DBMS
Score19.56
Rank#34  Overall
#21  Relational DBMS
Websitewww.databricks.comwww.enterprisedb.comspark.apache.org/­sql
Technical documentationdocs.databricks.comwww.enterprisedb.com/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperDatabricksEnterpriseDBApache Software Foundation
Initial release201320052014
Current release14, December 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercialcommercial infoBSD for PostgreSQL-componentsOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCScala
Server operating systemshostedLinux
Windows
Linux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyes
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.yesyes infospecific XML-type available, but no XML query functionality.no
Secondary indexesyesyesno
SQL infoSupport of SQLwith Databricks SQLyes infostandard with numerous extensionsSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
Supported programming languagesPython
R
Scala
.Net
C
C++
Delphi
Java
Perl
PHP
Python
Tcl
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions and aggregatesuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.no
Triggersyesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning infoby hash, list or rangeyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDno
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.nonono
User concepts infoAccess controlfine grained access rights according to SQL-standardno
More information provided by the system vendor
DatabricksEDB PostgresSpark SQL
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksEDB PostgresSpark SQL
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