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DBMS > Hypertable vs. jBASE vs. Kinetica vs. Microsoft Azure Table Storage vs. Splice Machine

System Properties Comparison Hypertable vs. jBASE vs. Kinetica vs. Microsoft Azure Table Storage vs. Splice Machine

Editorial information provided by DB-Engines
NameHypertable  Xexclude from comparisonjBASE  Xexclude from comparisonKinetica  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonSplice Machine  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionAn open source BigTable implementation based on distributed file systems such as HadoopA robust multi-value DBMS comprising development tools and middlewareFully vectorized database across both GPUs and CPUsA Wide Column Store for rapid development using massive semi-structured datasetsOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelWide column storeMultivalue DBMSRelational DBMSWide column storeRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.41
Rank#159  Overall
#3  Multivalue DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score4.48
Rank#75  Overall
#6  Wide column stores
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitewww.rocketsoftware.com/­products/­rocket-multivalue-application-development-platform/­rocket-jbasewww.kinetica.comazure.microsoft.com/­en-us/­services/­storage/­tablessplicemachine.com
Technical documentationdocs.rocketsoftware.com/­bundle?labelkey=jbase_5.9docs.kinetica.comsplicemachine.com/­how-it-works
DeveloperHypertable Inc.Rocket Software (formerly Zumasys)KineticaMicrosoftSplice Machine
Initial release20091991201220122014
Current release0.9.8.11, March 20165.77.1, August 20213.1, March 2021
License infoCommercial or Open SourceOpen Source infoGNU version 3. Commercial license availablecommercialcommercialcommercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C, C++Java
Server operating systemsLinux
OS X
Windows infoan inofficial Windows port is available
AIX
Linux
Windows
LinuxhostedLinux
OS X
Solaris
Windows
Data schemeschema-freeschema-freeyesschema-freeyes
Typing infopredefined data types such as float or datenooptionalyesyesyes
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 indexesrestricted infoonly exact value or prefix value scansyesnoyes
SQL infoSupport of SQLnoEmbedded SQL for jBASE in BASICSQL-like DML and DDL statementsnoyes
APIs and other access methodsC++ API
Thrift
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API
JDBC
ODBC
RESTful HTTP API
RESTful HTTP APIJDBC
Native Spark Datasource
ODBC
Supported programming languagesC++
Java
Perl
PHP
Python
Ruby
.Net
Basic
Jabbascript
Java
C++
Java
JavaScript (Node.js)
Python
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnoyesuser defined functionsnoyes infoJava
Triggersnoyesyes infotriggers when inserted values for one or more columns fall within a specified rangenoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding infoImplicit feature of the cloud serviceShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor on file system levelyesSource-replica replicationyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnononoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnooptimistic lockingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
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.yesyes infoGPU vRAM or System RAMnoyes
User concepts infoAccess controlnoAccess rights can be defined down to the item levelAccess rights for users and roles on table levelAccess rights based on private key authentication or shared access signaturesAccess rights for users, groups and roles according to SQL-standard

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More resources
HypertablejBASEKineticaMicrosoft Azure Table StorageSplice Machine
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