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DBMS > Google Cloud Bigtable vs. GridDB vs. SAP IQ vs. Sequoiadb

System Properties Comparison Google Cloud Bigtable vs. GridDB vs. SAP IQ vs. Sequoiadb

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonGridDB  Xexclude from comparisonSAP IQ infoformer name: Sybase IQ  Xexclude from comparisonSequoiadb  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 DataColumnar RDBMS optimized for Big Data analyticsNewSQL database with distributed OLTP and SQL
Primary database modelKey-value store
Wide column store
Time Series DBMSRelational DBMS infocolumn-orientedDocument store
Relational DBMS
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score2.64
Rank#108  Overall
#52  Relational DBMS
Score0.50
Rank#258  Overall
#41  Document stores
#120  Relational DBMS
Websitecloud.google.com/­bigtablegriddb.netwww.sap.com/­products/­technology-platform/­sybase-iq-big-data-management.htmlwww.sequoiadb.com
Technical documentationcloud.google.com/­bigtable/­docsdocs.griddb.nethelp.sap.com/­docs/­SAP_IQwww.sequoiadb.com/­en/­index.php?m=Files&a=index
DeveloperGoogleToshiba CorporationSAP, formerly SybaseSequoiadb Ltd.
Initial release2015201319942013
Current release5.1, August 202216.1 SPS04, April 2019
License infoCommercial or Open SourcecommercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercialOpen Source infoServer: AGPL; Client: Apache V2
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C++
Server operating systemshostedLinuxAIX
HP-UX
Linux
Solaris
Windows
Linux
Data schemeschema-freeyesyesschema-free
Typing infopredefined data types such as float or datenoyes infonumerical, string, blob, geometry, boolean, timestampyesyes infooid, date, timestamp, binary, regex
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)yesSQL-like query language
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
ADO.NET
JDBC
ODBC
RESTful HTTP API
proprietary protocol using JSON
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C
C#
C++
Java
Perl
PHP
Python
Ruby
.Net
C++
Java
PHP
Python
Server-side scripts infoStored proceduresnonoyesJavaScript
Triggersnoyesyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingshared disk or shared nothing architectures with SAP IQ MultiplexerSharding
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesSource-replica replicationSAP/Sybase Replication ServerSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsHadoop integrationno
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 ConsistencyEventual Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsACID at container levelACIDDocument is locked during a transaction
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.noyesnono
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 databasefine grained access rights according to SQL-standardsimple password-based access control
More information provided by the system vendor
Google Cloud BigtableGridDBSAP IQ infoformer name: Sybase IQSequoiadb
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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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
Google Cloud BigtableGridDBSAP IQ infoformer name: Sybase IQSequoiadb
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Google Introduces Autoscaling for Cloud Bigtable for Optimizing Costs
31 January 2022, InfoQ.com

Google scales up Cloud Bigtable NoSQL database
27 January 2022, TechTarget

Review: Google Bigtable scales with ease
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Google Cloud makes it cheaper to run smaller workloads on Bigtable
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Google introduces Cloud Bigtable managed NoSQL database to process data at scale
6 May 2015, VentureBeat

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