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DBMS > Kinetica vs. Microsoft Azure Table Storage vs. Oracle Berkeley DB vs. Riak TS vs. Spark SQL

System Properties Comparison Kinetica vs. Microsoft Azure Table Storage vs. Oracle Berkeley DB vs. Riak TS vs. Spark SQL

Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonRiak TS  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsA Wide Column Store for rapid development using massive semi-structured datasetsWidely used in-process key-value storeRiak TS is a distributed NoSQL database optimized for time series data and based on Riak KVSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSWide column storeKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Time Series DBMSRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score4.04
Rank#77  Overall
#6  Wide column stores
Score2.01
Rank#126  Overall
#21  Key-value stores
#3  Native XML DBMS
Score0.28
Rank#307  Overall
#27  Time Series DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitewww.kinetica.comazure.microsoft.com/­en-us/­services/­storage/­tableswww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlspark.apache.org/­sql
Technical documentationdocs.kinetica.comdocs.oracle.com/­cd/­E17076_05/­html/­index.htmlwww.tiot.jp/­riak-docs/­riak/­ts/­latestspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperKineticaMicrosoftOracle infooriginally developed by Sleepycat, which was acquired by OracleOpen Source, formerly Basho TechnologiesApache Software Foundation
Initial release20122012199420152014
Current release7.1, August 202118.1.40, May 20203.0.0, September 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialOpen Source infocommercial license availableOpen SourceOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++C, Java, C++ (depending on the Berkeley DB edition)ErlangScala
Server operating systemsLinuxhostedAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Linux
OS X
Linux
OS X
Windows
Data schemeyesschema-freeschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyesnonoyes
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.nonoyes infoonly with the Berkeley DB XML editionnono
Secondary indexesyesnoyesrestrictedno
SQL infoSupport of SQLSQL-like DML and DDL statementsnoyes infoSQL interfaced based on SQLite is availableyes, limitedSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
RESTful HTTP APIHTTP API
Native Erlang Interface
JDBC
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsnonoErlangno
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenoyes infoonly for the SQL APIyes infopre-commit hooks and post-commit hooksno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud servicenoneShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationselectable replication factornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyesnonono infolinks between datasets can be storedno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanooptimistic lockingACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoGPU vRAM or System RAMnoyesno
User concepts infoAccess controlAccess rights for users and roles on table levelAccess rights based on private key authentication or shared access signaturesnonono

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More resources
KineticaMicrosoft Azure Table StorageOracle Berkeley DBRiak TSSpark SQL
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