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DBMS > Datomic vs. Google Cloud Bigtable vs. Google Cloud Datastore vs. PouchDB

System Properties Comparison Datomic vs. Google Cloud Bigtable vs. Google Cloud Datastore vs. PouchDB

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonPouchDB  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformJavaScript DBMS with an API inspired by CouchDB
Primary database modelRelational DBMSKey-value store
Wide column store
Document storeDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score4.47
Rank#76  Overall
#12  Document stores
Score2.28
Rank#115  Overall
#21  Document stores
Websitewww.datomic.comcloud.google.com/­bigtablecloud.google.com/­datastorepouchdb.com
Technical documentationdocs.datomic.comcloud.google.com/­bigtable/­docscloud.google.com/­datastore/­docspouchdb.com/­guides
DeveloperCognitectGoogleGoogleApache Software Foundation
Initial release2012201520082012
Current release1.0.6735, June 20237.1.1, June 2019
License infoCommercial or Open Sourcecommercial infolimited edition freecommercialcommercialOpen Source
Cloud-based only infoOnly available as a cloud servicenoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ClojureJavaScript
Server operating systemsAll OS with a Java VMhostedhostedserver-less, requires a JavaScript environment (browser, Node.js)
Data schemeyesschema-freeschema-freeschema-free
Typing infopredefined data types such as float or dateyesnoyes, details hereno
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 indexesyesnoyesyes infovia views
SQL infoSupport of SQLnonoSQL-like query language (GQL)no
APIs and other access methodsRESTful HTTP APIgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
HTTP REST infoonly for PouchDB Server
JavaScript API
Supported programming languagesClojure
Java
C#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
JavaScript
Server-side scripts infoStored proceduresyes infoTransaction Functionsnousing Google App EngineView functions in JavaScript
TriggersBy using transaction functionsnoCallbacks using the Google Apps Engineyes
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingShardingSharding infowith a proxy-based framework, named couchdb-lounge
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersInternal replication in Colossus, and regional replication between two clusters in different zonesMulti-source replication using PaxosMulti-source replication infoalso with CouchDB databases
Source-replica replication infoalso with CouchDB databases
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infousing Google Cloud Dataflowyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate 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 Consistency
Foreign keys infoReferential integritynonoyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyes infoby using IndexedDB, WebSQL or LevelDB as backend
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes inforecommended only for testing and developmentnonoyes
User concepts infoAccess controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)no

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DatomicGoogle Cloud BigtableGoogle Cloud DatastorePouchDB
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