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DBMS > Databricks vs. Kinetica vs. SwayDB vs. Valentina Server

System Properties Comparison Databricks vs. Kinetica vs. SwayDB vs. Valentina Server

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonKinetica  Xexclude from comparisonSwayDB  Xexclude from comparisonValentina Server  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.Fully vectorized database across both GPUs and CPUsAn embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storageObject-relational database and reports server
Primary database modelDocument store
Relational DBMS
Relational DBMSKey-value storeRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score84.24
Rank#14  Overall
#2  Document stores
#9  Relational DBMS
Score0.42
Rank#261  Overall
#120  Relational DBMS
Score0.00
Rank#384  Overall
#60  Key-value stores
Score0.18
Rank#313  Overall
#140  Relational DBMS
Websitewww.databricks.comwww.kinetica.comswaydb.simer.auwww.valentina-db.net
Technical documentationdocs.databricks.comdocs.kinetica.comvalentina-db.com/­docs/­dokuwiki/­v5/­doku.php
DeveloperDatabricksKineticaSimer PlahaParadigma Software
Initial release2013201220181999
Current release7.1, August 20215.7.5
License infoCommercial or Open SourcecommercialcommercialOpen Source infoGNU Affero GPL V3.0commercial
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++Scala
Server operating systemshostedLinuxLinux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-freeyes
Typing infopredefined data types such as float or dateyesnoyes
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.yesnono
Secondary indexesyesyesnoyes
SQL infoSupport of SQLwith Databricks SQLSQL-like DML and DDL statementsnoyes
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
ODBC
Supported programming languagesPython
R
Scala
C++
Java
JavaScript (Node.js)
Python
Java
Kotlin
Scala
.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET
Server-side scripts infoStored proceduresuser defined functions and aggregatesuser defined functionsnoyes
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenoyes
Partitioning methods infoMethods for storing different data on different nodesShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integrityyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoAtomic execution of operations
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoGPU vRAM or System RAMyesyes
User concepts infoAccess controlAccess rights for users and roles on table levelnofine grained access rights according to SQL-standard
More information provided by the system vendor
DatabricksKineticaSwayDBValentina Server
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksKineticaSwayDBValentina Server
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