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DBMS > Google Cloud Datastore vs. Microsoft Azure Table Storage vs. OrigoDB vs. PouchDB vs. STSdb

System Properties Comparison Google Cloud Datastore vs. Microsoft Azure Table Storage vs. OrigoDB vs. PouchDB vs. STSdb

Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonOrigoDB  Xexclude from comparisonPouchDB  Xexclude from comparisonSTSdb  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA Wide Column Store for rapid development using massive semi-structured datasetsA fully ACID in-memory object graph databaseJavaScript DBMS with an API inspired by CouchDBKey-Value Store with special method for indexing infooptimized for high performance using a special indexing method
Primary database modelDocument storeWide column storeDocument store
Object oriented DBMS
Document storeKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score4.48
Rank#75  Overall
#6  Wide column stores
Score0.00
Rank#383  Overall
#53  Document stores
#20  Object oriented DBMS
Score2.28
Rank#115  Overall
#21  Document stores
Score0.04
Rank#360  Overall
#52  Key-value stores
Websitecloud.google.com/­datastoreazure.microsoft.com/­en-us/­services/­storage/­tablesorigodb.compouchdb.comgithub.com/­STSSoft/­STSdb4
Technical documentationcloud.google.com/­datastore/­docsorigodb.com/­docspouchdb.com/­guides
DeveloperGoogleMicrosoftRobert Friberg et alApache Software FoundationSTS Soft SC
Initial release200820122009 infounder the name LiveDB20122011
Current release7.1.1, June 20194.0.8, September 2015
License infoCommercial or Open SourcecommercialcommercialOpen SourceOpen SourceOpen Source infoGPLv2, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC#JavaScriptC#
Server operating systemshostedhostedLinux
Windows
server-less, requires a JavaScript environment (browser, Node.js)Windows
Data schemeschema-freeschema-freeyesschema-freeyes
Typing infopredefined data types such as float or dateyes, details hereyesUser defined using .NET types and collectionsnoyes infoprimitive types and user defined types (classes)
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.nonono infocan be achieved using .NETno
Secondary indexesyesnoyesyes infovia viewsno
SQL infoSupport of SQLSQL-like query language (GQL)nononono
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
RESTful HTTP API.NET Client API
HTTP API
LINQ
HTTP REST infoonly for PouchDB Server
JavaScript API
.NET Client API
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
.NetJavaScriptC#
Java
Server-side scripts infoStored proceduresusing Google App EnginenoyesView functions in JavaScriptno
TriggersCallbacks using the Google Apps Enginenoyes infoDomain Eventsyesno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud servicehorizontal partitioning infoclient side managed; servers are not synchronizedSharding infowith a proxy-based framework, named couchdb-loungenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationMulti-source replication infoalso with CouchDB databases
Source-replica replication infoalso with CouchDB databases
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownonoyesno
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.Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnodepending on modelnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsoptimistic lockingACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoWrite ahead logyes infoby using IndexedDB, WebSQL or LevelDB as backendyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights based on private key authentication or shared access signaturesRole based authorizationnono

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More resources
Google Cloud DatastoreMicrosoft Azure Table StorageOrigoDBPouchDBSTSdb
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