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DBMS > Drizzle vs. Google Cloud Datastore vs. IBM Cloudant vs. Kinetica vs. SiteWhere

System Properties Comparison Drizzle vs. Google Cloud Datastore vs. IBM Cloudant vs. Kinetica vs. SiteWhere

Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonIBM Cloudant  Xexclude from comparisonKinetica  Xexclude from comparisonSiteWhere  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformDatabase as a Service offering based on Apache CouchDBFully vectorized database across both GPUs and CPUsM2M integration platform for persisting/querying time series data
Primary database modelRelational DBMSDocument storeDocument storeRelational DBMSTime Series DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score2.68
Rank#106  Overall
#20  Document stores
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score0.06
Rank#356  Overall
#35  Time Series DBMS
Websitecloud.google.com/­datastorewww.ibm.com/­products/­cloudantwww.kinetica.comgithub.com/­sitewhere/­sitewhere
Technical documentationcloud.google.com/­datastore/­docscloud.ibm.com/­docs/­Cloudantdocs.kinetica.comsitewhere1.sitewhere.io/­index.html
DeveloperDrizzle project, originally started by Brian AkerGoogleIBM, Apache Software Foundation infoIBM acquired Cloudant in February 2014KineticaSiteWhere
Initial release20082008201020122010
Current release7.2.4, September 20127.1, August 2021
License infoCommercial or Open SourceOpen Source infoGNU GPLcommercialcommercialcommercialOpen Source infoCommon Public Attribution License Version 1.0
Cloud-based only infoOnly available as a cloud servicenoyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++ErlangC, C++Java
Server operating systemsFreeBSD
Linux
OS X
hostedhostedLinuxLinux
OS X
Windows
Data schemeyesschema-freeschema-freeyespredefined scheme
Typing infopredefined data types such as float or dateyesyes, details herenoyesyes
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 indexesyesyesyesyesno
SQL infoSupport of SQLyes infowith proprietary extensionsSQL-like query language (GQL)noSQL-like DML and DDL statementsno
APIs and other access methodsJDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
RESTful HTTP/JSON APIJDBC
ODBC
RESTful HTTP API
HTTP REST
Supported programming languagesC
C++
Java
PHP
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
Java
JavaScript
Objective-C
PHP
Ruby
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresnousing Google App EngineView functions (Map-Reduce) in JavaScriptuser defined functions
Triggersno infohooks for callbacks inside the server can be used.Callbacks using the Google Apps Engineyesyes infotriggers when inserted values for one or more columns fall within a specified range
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingShardingSharding infobased on HBase
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replication using PaxosMulti-source replication
Source-replica replication
Source-replica replicationselectable replication factor infobased on HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Eventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integrityyesyes infovia ReferenceProperties or Ancestor pathsnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsno infoatomic operations within a document possiblenono
Concurrency infoSupport for concurrent manipulation of datayesyesyes infoOptimistic lockingyesyes
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.nonoyes infoGPU vRAM or System RAMno
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess 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 levelUsers with fine-grained authorization concept

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More resources
DrizzleGoogle Cloud DatastoreIBM CloudantKineticaSiteWhere
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