DBMS > Atos Standard Common Repository vs. EsgynDB vs. Google Cloud Datastore vs. Warp 10

System Properties Comparison Atos Standard Common Repository vs. EsgynDB vs. Google Cloud Datastore vs. Warp 10

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Editorial information provided by DB-Engines
Name	Atos Standard Common Repository  Xexclude from comparison	EsgynDB  Xexclude from comparison	Google Cloud Datastore  Xexclude from comparison	Warp 10  Xexclude from comparison
	This system has been discontinued and will be removed from the DB-Engines ranking.
Description	Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks	Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion	Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform	TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase
Primary database model	Document store Key-value store	Relational DBMS	Document store	Time Series DBMS
DB-Engines Ranking measures the popularity of database management systems Trend Chart		Score 0.25 Rank #312   Overall #138   Relational DBMS	Score 4.36 Rank #72   Overall #12   Document stores	Score 0.14 Rank #344   Overall #32   Time Series DBMS
Website	atos.net/en/convergence-creators/portfolio/standard-common-repository	www.esgyn.cn	cloud.google.com/­datastore	www.warp10.io
Technical documentation			cloud.google.com/­datastore/­docs	www.warp10.io/­content/­02_Getting_started
Developer	Atos Convergence Creators	Esgyn	Google	SenX
Initial release	2016	2015	2008	2015
Current release	1703
License Commercial or Open Source	commercial	commercial	commercial	Open Source Apache License 2.0
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++, Java		Java
Server operating systems	Linux	Linux	hosted	Linux OS X Windows
Data scheme	Schema and schema-less with LDAP views	yes	schema-free	schema-free
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	no
Secondary indexes	yes	yes	yes	no
SQL Support of SQL	no	yes	SQL-like query language (GQL)	no
APIs and other access methods	LDAP	ADO.NET JDBC ODBC	gRPC (using protocol buffers) API RESTful HTTP/JSON API	HTTP API Jupyter WebSocket
Supported programming languages	All languages with LDAP bindings	All languages supporting JDBC/ODBC/ADO.Net	.Net Go Java JavaScript (Node.js) PHP Python Ruby
Server-side scripts Stored procedures	no	Java Stored Procedures	using Google App Engine	yes WarpScript
Triggers	yes	no	Callbacks using the Google Apps Engine	no
Partitioning methods Methods for storing different data on different nodes	Sharding cell division	Sharding	Sharding	Sharding based on HBase
Replication methods Methods for redundantly storing data on multiple nodes	yes	Multi-source replication between multi datacenters	Multi-source replication using Paxos	selectable replication factor based on HBase
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	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.	Immediate Consistency based on HBase
Foreign keys Referential integrity	no	yes	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	ACID	ACID Serializable Isolation within Transactions, Read Committed outside of Transactions	no
Concurrency Support for concurrent manipulation of data	yes	yes	yes	yes
Durability Support for making data persistent	yes	yes	yes	yes
In-memory capabilities Is there an option to define some or all structures to be held in-memory only.	yes	no	no	yes
User concepts Access control	LDAP bind authentication	fine grained access rights according to SQL-standard	Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)	Mandatory use of cryptographic tokens, containing fine-grained authorizations
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More resources
	Atos Standard Common Repository	EsgynDB	Google Cloud Datastore	Warp 10
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