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

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

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Editorial information provided by DB-Engines
NameAmazon Aurora  Xexclude from comparisonAmazon DocumentDB  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.
DescriptionMySQL and PostgreSQL compatible cloud service by AmazonFast, scalable, highly available, and fully managed MongoDB-compatible database serviceA .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 modelRelational DBMSDocument storeDocument store
Key-value store
Document store
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.91
Rank#50  Overall
#32  Relational DBMS
Score1.91
Rank#132  Overall
#24  Document stores
Score4.47
Rank#76  Overall
#12  Document stores
Websiteaws.amazon.com/­rds/­auroraaws.amazon.com/­documentdbcloud.google.com/­datastore
Technical documentationdocs.aws.amazon.com/­AmazonRDS/­latest/­AuroraUserGuide/­CHAP_Aurora.htmlaws.amazon.com/­documentdb/­resourcescloud.google.com/­datastore/­docs
DeveloperAmazonFatCloudGoogle
Initial release2015201920122008
License infoCommercial or Open Sourcecommercialcommercialcommercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesyesnoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC#
Server operating systemshostedhostedWindowshosted
Data schemeyesschema-freeschema-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.yesnono
Secondary indexesyesyesyesyes
SQL infoSupport of SQLyesnono infoVia inetgration in SQL ServerSQL-like query language (GQL)
APIs and other access methodsADO.NET
JDBC
ODBC
proprietary protocol using JSON (MongoDB compatible).NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Supported programming languagesAda
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Go
Java
JavaScript (Node.js)
PHP
Python
C#.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresyesnoyes infovia applicationsusing Google App Engine
Triggersyesnoyes infovia applicationsCallbacks using the Google Apps Engine
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningnoneShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasselectable replication factorMulti-source replication using Paxos
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)yesyes 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 integrityyesno infotypically not used, however similar functionality with DBRef possiblenoyes infovia ReferenceProperties or Ancestor paths
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-document operationsnoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactions
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.yesno
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users and rolesno 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 AuroraAmazon DocumentDBFatDBGoogle Cloud Datastore
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