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DBMS > Google Cloud Bigtable vs. Kinetica vs. OpenMLDB vs. TimesTen

System Properties Comparison Google Cloud Bigtable vs. Kinetica vs. OpenMLDB vs. TimesTen

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonKinetica  Xexclude from comparisonOpenMLDB  Xexclude from comparisonTimesTen  Xexclude from comparison
DescriptionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Fully vectorized database across both GPUs and CPUsAn open-source machine learning database that provides a feature platform for training and inferenceAn in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.
Primary database modelKey-value store
Wide column store
Relational DBMSTime Series DBMSRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.97
Rank#92  Overall
#15  Key-value stores
#8  Wide column stores
Score0.42
Rank#261  Overall
#120  Relational DBMS
Score0.11
Rank#339  Overall
#30  Time Series DBMS
Score1.26
Rank#164  Overall
#75  Relational DBMS
Websitecloud.google.com/­bigtablewww.kinetica.comopenmldb.aiwww.oracle.com/­database/­technologies/­related/­timesten.html
Technical documentationcloud.google.com/­bigtable/­docsdocs.kinetica.comopenmldb.ai/­docs/­zh/­maindocs.oracle.com/­en/­database/­other-databases/­timesten/­index.html
DeveloperGoogleKinetica4 Paradigm Inc.Oracle infooriginally founded in HP Labs it was acquired by Oracle in 2005
Initial release2015201220201998
Current release7.1, August 20212024-2 February 2024Release 22.1
License infoCommercial or Open SourcecommercialcommercialOpen Sourcecommercial
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++C++, Java, Scala
Server operating systemshostedLinuxLinuxIBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64
Data schemeschema-freeyesFixed schemayes
Typing infopredefined data types such as float or datenoyesyesyes
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.nononono
Secondary indexesnoyesyesyes
SQL infoSupport of SQLnoSQL-like DML and DDL statementsyesyes
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
RESTful HTTP API
JDBC
SQLAlchemy
ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
C++
Java
JavaScript (Node.js)
Python
C++
Go
Java
Python
Scala
C
C++
Java
Node.js
PL/SQL
Python
Server-side scripts infoStored proceduresnouser defined functionsnoPL/SQL
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangenono
Partitioning methods infoMethods for storing different data on different nodesShardingShardinghorizontal partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesSource-replica replicationSource-replica replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoby means of logfiles and checkpoints
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, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users and roles on table levelfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
Google Cloud BigtableKineticaOpenMLDBTimesTen
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