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DBMS > Databricks vs. Infobright vs. Postgres-XL

System Properties Comparison Databricks vs. Infobright vs. Postgres-XL

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonInfobright  Xexclude from comparisonPostgres-XL  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.High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontendBased on PostgreSQL enhanced with MPP and write-scale-out cluster features
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
Score60.33
Rank#19  Overall
#3  Document stores
#13  Relational DBMS
Score1.47
Rank#174  Overall
#86  Relational DBMS
Score0.71
Rank#251  Overall
#118  Relational DBMS
Websitedatabricks.comignitetech.com/­softwarelibrary/­infobrightdbwww.postgres-xl.org
Technical documentationdatabricks.com/­documentationwww.postgres-xl.org/­documentation
DeveloperDatabricksIgnite Technologies Inc.; formerly InfoBright Inc.
Initial release201320052014 infosince 2012, originally named StormDB
Current release10 R1, October 2018
License infoCommercial or Open SourceOpen Sourcecommercial infoThe open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016Open Source infoMozilla public license
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCC
Server operating systemshostedLinux
Windows
Linux
macOS
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.yesnoyes infoXML type, but no XML query functionality
Secondary indexesyesno infoKnowledge Grid Technology used insteadyes
SQL infoSupport of SQLwith Databricks SQLyesyes infodistributed, parallel query execution
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesPython
R
Scala
.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Server-side scripts infoStored proceduresuser defined functions and aggregatesnouser defined functions
Triggersnoyes
Partitioning methods infoMethods for storing different data on different nodesnonehorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID infoMVCC
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.noyesno
User concepts infoAccess controlfine grained access rights according to SQL-standard infoexploiting MySQL or PostgreSQL frontend capabilitiesfine grained access rights according to SQL-standard

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More resources
DatabricksInfobrightPostgres-XL
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Expanded AtScale, Databricks integration adds functionality
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