DBMS > Atos Standard Common Repository vs. FatDB vs. Google Cloud Datastore vs. IBM Db2 Event Store

System Properties Comparison Atos Standard Common Repository vs. FatDB vs. Google Cloud Datastore vs. IBM Db2 Event Store

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Editorial information provided by DB-Engines
Name	Atos Standard Common Repository  Xexclude from comparison	FatDB  Xexclude from comparison	Google Cloud Datastore  Xexclude from comparison	IBM Db2 Event Store  Xexclude from comparison
	This system has been discontinued and will be removed from the DB-Engines ranking.	FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
Description	Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks	A .NET NoSQL DBMS that can integrate with and extend SQL Server.	Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform	Distributed Event Store optimized for Internet of Things use cases
Primary database model	Document store Key-value store	Document store Key-value store	Document store	Event Store Time Series DBMS
DB-Engines Ranking measures the popularity of database management systems Trend Chart			Score 4.36 Rank #72   Overall #12   Document stores	Score 0.27 Rank #309   Overall #2   Event Stores #28   Time Series DBMS
Website	atos.net/en/convergence-creators/portfolio/standard-common-repository		cloud.google.com/­datastore	www.ibm.com/­products/­db2-event-store
Technical documentation			cloud.google.com/­datastore/­docs	www.ibm.com/­docs/­en/­db2-event-store
Developer	Atos Convergence Creators	FatCloud	Google	IBM
Initial release	2016	2012	2008	2017
Current release	1703			2.0
License Commercial or Open Source	commercial	commercial	commercial	commercial free developer edition available
Cloud-based only Only available as a cloud service	no	no	yes	no
DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed.
Implementation language	Java	C#		C and C++
Server operating systems	Linux	Windows	hosted	Linux Linux, macOS, Windows for the developer addition
Data scheme	Schema and schema-less with LDAP views	schema-free	schema-free	yes
Typing predefined data types such as float or date	optional	yes	yes, details here	yes
XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.	yes		no	no
Secondary indexes	yes	yes	yes	no
SQL Support of SQL	no	no Via inetgration in SQL Server	SQL-like query language (GQL)	yes through the embedded Spark runtime
APIs and other access methods	LDAP	.NET Client API LINQ RESTful HTTP API RPC Windows WCF Bindings	gRPC (using protocol buffers) API RESTful HTTP/JSON API	ADO.NET DB2 Connect JDBC ODBC RESTful HTTP API
Supported programming languages	All languages with LDAP bindings	C#	.Net Go Java JavaScript (Node.js) PHP Python Ruby	C C# C++ Cobol Delphi Fortran Go Java JavaScript (Node.js) Perl PHP Python R Ruby Scala Visual Basic
Server-side scripts Stored procedures	no	yes via applications	using Google App Engine	yes
Triggers	yes	yes via applications	Callbacks using the Google Apps Engine	no
Partitioning methods Methods for storing different data on different nodes	Sharding cell division	Sharding	Sharding	Sharding
Replication methods Methods for redundantly storing data on multiple nodes	yes	selectable replication factor	Multi-source replication using Paxos	Active-active shard replication
MapReduce Offers an API for user-defined Map/Reduce methods		yes	yes using Google Cloud Dataflow	no
Consistency concepts Methods to ensure consistency in a distributed system	Immediate Consistency or Eventual Consistency depending on configuration	Eventual Consistency Immediate Consistency	Immediate Consistency or Eventual Consistency depending on type of query and configuration Strong 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 Referential integrity	no	no	yes via ReferenceProperties or Ancestor paths	no
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data	Atomic execution of specific operations	no	ACID Serializable Isolation within Transactions, Read Committed outside of Transactions	no
Concurrency Support for concurrent manipulation of data	yes	yes	yes	No - written data is immutable
Durability Support for making data persistent	yes	yes	yes	Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage
In-memory capabilities Is there an option to define some or all structures to be held in-memory only.	yes		no	yes
User concepts Access control	LDAP bind authentication	no Can implement custom security layer via applications	Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)	fine grained access rights according to SQL-standard
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More resources
	Atos Standard Common Repository	FatDB	Google Cloud Datastore	IBM Db2 Event Store
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