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DBMS > Databend vs. Kinetica vs. Rockset vs. Spark SQL

System Properties Comparison Databend vs. Kinetica vs. Rockset vs. Spark SQL

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Editorial information provided by DB-Engines
NameDatabend  Xexclude from comparisonKinetica  Xexclude from comparisonRockset  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionAn open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexityFully vectorized database across both GPUs and CPUsA scalable, reliable search and analytics service in the cloud, built on RocksDBSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRelational DBMSDocument storeRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Relational DBMS
Search engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.34
Rank#283  Overall
#130  Relational DBMS
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score0.82
Rank#212  Overall
#36  Document stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitegithub.com/­datafuselabs/­databend
www.databend.com
www.kinetica.comrockset.comspark.apache.org/­sql
Technical documentationdocs.databend.comdocs.kinetica.comdocs.rockset.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperDatabend LabsKineticaRocksetApache Software Foundation
Initial release2021201220192014
Current release1.0.59, April 20237.1, August 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageRustC, C++C++Scala
Server operating systemshosted
Linux
macOS
LinuxhostedLinux
OS X
Windows
Data schemeyesyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesdynamic typingyes
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.nonono infoingestion from XML files supportedno
Secondary indexesnoyesall fields are automatically indexedno
SQL infoSupport of SQLyesSQL-like DML and DDL statementsRead-only SQL queries, including JOINsSQL-like DML and DDL statements
APIs and other access methodsCLI Client
JDBC
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
HTTP RESTJDBC
ODBC
Supported programming languagesGo
Java
JavaScript (Node.js)
Python
Rust
C++
Java
JavaScript (Node.js)
Python
Go
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresnouser defined functionsnono
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangenono
Partitioning methods infoMethods for storing different data on different nodesnoneShardingAutomatic shardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesnoneSource-replica replicationyesnone
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 configurationEventual Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesnonono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.yes infoGPU vRAM or System RAMno
User concepts infoAccess controlUsers with fine-grained authorization concept, user rolesAccess rights for users and roles on table levelAccess rights for users and organizations can be defined via Rockset consoleno

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DatabendKineticaRocksetSpark SQL
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