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DBMS > Kinetica vs. Rockset vs. Vertica vs. Yanza

System Properties Comparison Kinetica vs. Rockset vs. Vertica vs. Yanza

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonRockset  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparisonYanza  Xexclude from comparison
Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionFully vectorized database across both GPUs and CPUsA scalable, reliable search and analytics service in the cloud, built on RocksDBCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.Time Series DBMS for IoT Applications
Primary database modelRelational DBMSDocument storeRelational DBMS infoColumn orientedTime Series DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Relational DBMS
Search engine
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score0.79
Rank#211  Overall
#35  Document stores
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websitewww.kinetica.comrockset.comwww.vertica.comyanza.com
Technical documentationdocs.kinetica.comdocs.rockset.comvertica.com/­documentation
DeveloperKineticaRocksetOpenText infopreviously Micro Focus and Hewlett PackardYanza
Initial release2012201920052015
Current release7.1, August 202112.0.3, January 2023
License infoCommercial or Open Sourcecommercialcommercialcommercial infoLimited community edition freecommercial infofree version available
Cloud-based only infoOnly available as a cloud servicenoyesno infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containersno infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++C++C++
Server operating systemsLinuxhostedLinuxWindows
Data schemeyesschema-freeYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.schema-free
Typing infopredefined data types such as float or dateyesdynamic typingyesno
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.nono infoingestion from XML files supportednono
Secondary indexesyesall fields are automatically indexedNo Indexes Required. Different internal optimization strategy, but same functionality included.no
SQL infoSupport of SQLSQL-like DML and DDL statementsRead-only SQL queries, including JOINsFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.no
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
HTTP RESTADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
HTTP API
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
Go
Java
JavaScript (Node.js)
Python
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
any language that supports HTTP calls
Server-side scripts infoStored proceduresuser defined functionsnoyes, PostgreSQL PL/pgSQL, with minor differencesno
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenoyes, called Custom Alertsyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodesShardingAutomatic shardinghorizontal partitioning, hierarchical partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyesMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono infoBi-directional Spark integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDno
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 controlAccess rights for users and roles on table levelAccess rights for users and organizations can be defined via Rockset consolefine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hashno
More information provided by the system vendor
KineticaRocksetVertica infoOpenText™ Vertica™Yanza
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more
Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more
Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
» more

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More resources
KineticaRocksetVertica infoOpenText™ Vertica™Yanza
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