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DBMS > Google Cloud Bigtable vs. GridDB vs. Kinetica vs. XTDB

System Properties Comparison Google Cloud Bigtable vs. GridDB vs. Kinetica vs. XTDB

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonGridDB  Xexclude from comparisonKinetica  Xexclude from comparisonXTDB infoformerly named Crux  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.Scalable in-memory time series database optimized for IoT and Big DataFully vectorized database across both GPUs and CPUsA general purpose database with bitemporal SQL and Datalog and graph queries
Primary database modelKey-value store
Wide column store
Time Series DBMSRelational DBMSDocument store
Secondary database modelsKey-value store
Relational DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.58
Rank#92  Overall
#14  Key-value stores
#8  Wide column stores
Score2.02
Rank#132  Overall
#11  Time Series DBMS
Score0.69
Rank#234  Overall
#107  Relational DBMS
Score0.09
Rank#351  Overall
#47  Document stores
Websitecloud.google.com/­bigtablegriddb.netwww.kinetica.comgithub.com/­xtdb/­xtdb
www.xtdb.com
Technical documentationcloud.google.com/­bigtable/­docsdocs.griddb.netdocs.kinetica.comwww.xtdb.com/­docs
DeveloperGoogleToshiba CorporationKineticaJuxt Ltd.
Initial release2015201320122019
Current release5.1, August 20227.1, August 20211.19, September 2021
License infoCommercial or Open SourcecommercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercialOpen Source infoMIT License
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++Clojure
Server operating systemshostedLinuxLinuxAll OS with a Java 8 (and higher) VM
Linux
Data schemeschema-freeyesyesschema-free
Typing infopredefined data types such as float or datenoyes infonumerical, string, blob, geometry, boolean, timestampyesyes, extensible-data-notation format
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 SQLnoSQL92, SQL-like TQL (Toshiba Query Language)SQL-like DML and DDL statementslimited SQL, making use of Apache Calcite
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JDBC
ODBC
RESTful HTTP API
HTTP REST
JDBC
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C++
Java
JavaScript (Node.js)
Python
Clojure
Java
Server-side scripts infoStored proceduresnonouser defined functionsno
Triggersnoyesyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingnone
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 replicationyes, each node contains all data
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsnono
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 within container, eventual consistency across containersImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsACID at container levelnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes infoGPU vRAM or System RAM
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users can be defined per databaseAccess rights for users and roles on table level
More information provided by the system vendor
Google Cloud BigtableGridDBKineticaXTDB infoformerly named Crux
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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More resources
Google Cloud BigtableGridDBKineticaXTDB infoformerly named Crux
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