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DBMS > Citus vs. Databricks vs. Teradata

System Properties Comparison Citus vs. Databricks vs. Teradata

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Editorial information provided by DB-Engines
NameCitus  Xexclude from comparisonDatabricks  Xexclude from comparisonTeradata  Xexclude from comparison
DescriptionScalable 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.A hybrid cloud data analytics software platform (Teradata Vantage)
Primary database modelRelational DBMSDocument store
Relational DBMS
Relational DBMS
Secondary database modelsDocument storeDocument store
Graph DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.15
Rank#117  Overall
#56  Relational DBMS
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score44.87
Rank#22  Overall
#15  Relational DBMS
Websitewww.citusdata.comwww.databricks.comwww.teradata.com
Technical documentationdocs.citusdata.comdocs.databricks.comdocs.teradata.com
DeveloperDatabricksTeradata
Initial release201020131984
Current release8.1, December 2018Teradata Vantage 1.0 MU2, January 2019
License infoCommercial or Open SourceOpen Source infoAGPL, commercial license also availablecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC
Server operating systemsLinuxhostedhosted
Linux
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)yes
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 functionalityyesyes
Secondary indexesyesyesyes infoJoin-index to prejoin tables, aggregate index, sparse index, hash index
SQL infoSupport of SQLyes infostandard, with numerous extensionswith Databricks SQLyes infoSQL 2016 + extensions
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
RESTful HTTP API
.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Python
R
Scala
C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby
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 aggregatesyes infoUDFs, stored procedures, table functions in parallel
Triggersyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoHashing
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replication infoother methods possible by using 3rd party extensionsyesMulti-source replication
Source-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 integrityyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID
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.nonoyes
User concepts infoAccess controlfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard
More information provided by the system vendor
CitusDatabricksTeradata
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
CitusDatabricksTeradata
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