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DBMS > Amazon DocumentDB vs. Datomic vs. FatDB vs. Google Cloud Datastore

System Properties Comparison Amazon DocumentDB vs. Datomic vs. FatDB vs. Google Cloud Datastore

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Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonDatomic  Xexclude from comparisonFatDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityA .NET NoSQL DBMS that can integrate with and extend SQL Server.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform
Primary database modelDocument storeRelational DBMSDocument store
Key-value store
Document store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#132  Overall
#24  Document stores
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Websiteaws.amazon.com/­documentdbwww.datomic.comcloud.google.com/­datastore
Technical documentationaws.amazon.com/­documentdb/­resourcesdocs.datomic.comcloud.google.com/­datastore/­docs
DeveloperCognitectFatCloudGoogle
Initial release2019201220122008
Current release1.0.6735, June 2023
License infoCommercial or Open Sourcecommercialcommercial infolimited edition freecommercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesnonoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ClojureC#
Server operating systemshostedAll OS with a Java VMWindowshosted
Data schemeschema-freeyesschema-freeschema-free
Typing infopredefined data types such as float or dateyesyesyesyes, details here
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
Secondary indexesyesyesyesyes
SQL infoSupport of SQLnonono infoVia inetgration in SQL ServerSQL-like query language (GQL)
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)RESTful HTTP API.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
Clojure
Java
C#.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresnoyes infoTransaction Functionsyes infovia applicationsusing Google App Engine
TriggersnoBy using transaction functionsyes infovia applicationsCallbacks using the Google Apps Engine
Partitioning methods infoMethods for storing different data on different nodesnonenone infoBut extensive use of caching in the application peersShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasnone infoBut extensive use of caching in the application peersselectable replication factorMulti-source replication using Paxos
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)noyesyes infousing Google Cloud Dataflow
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
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.
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenonoyes infovia ReferenceProperties or Ancestor paths
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsACIDnoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes
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 developmentno
User concepts infoAccess controlAccess rights for users and rolesnono infoCan implement custom security layer via applicationsAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources
Amazon DocumentDBDatomicFatDBGoogle Cloud Datastore
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