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DBMS > Apache Pinot vs. Citus vs. Databricks

System Properties Comparison Apache Pinot vs. Citus vs. Databricks

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Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonCitus  Xexclude from comparisonDatabricks  Xexclude from comparison
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencyScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLThe 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.
Primary database modelRelational DBMSRelational DBMSDocument store
Relational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.38
Rank#275  Overall
#126  Relational DBMS
Score2.15
Rank#117  Overall
#56  Relational DBMS
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Websitepinot.apache.orgwww.citusdata.comwww.databricks.com
Technical documentationdocs.pinot.apache.orgdocs.citusdata.comdocs.databricks.com
DeveloperApache Software Foundation and contributorsDatabricks
Initial release201520102013
Current release1.0.0, September 20238.1, December 2018
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoAGPL, commercial license also availablecommercial
Cloud-based only infoOnly available as a cloud servicenonoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC
Server operating systemsAll OS with a Java JDK11 or higherLinuxhosted
Data schemeyesyesFlexible Schema (defined schema, partial schema, schema free)
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.yes infospecific XML type available, but no XML query functionalityyes
Secondary indexesyesyes
SQL infoSupport of SQLSQL-like query languageyes infostandard, with numerous extensionswith Databricks SQL
APIs and other access methodsJDBCADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
RESTful HTTP API
Supported programming languagesGo
Java
Python
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.user defined functions and aggregates
Triggersyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replication infoother methods possible by using 3rd party extensionsyes
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 integrityyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID
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.nono
User concepts infoAccess controlfine grained access rights according to SQL-standard
More information provided by the system vendor
Apache PinotCitusDatabricks
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Apache PinotCitusDatabricks
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