DBMS > Atos Standard Common Repository vs. Axibase vs. Google Cloud Datastore vs. JaguarDB

System Properties Comparison Atos Standard Common Repository vs. Axibase vs. Google Cloud Datastore vs. JaguarDB

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Editorial information provided by DB-Engines
Name	Atos Standard Common Repository  Xexclude from comparison	Axibase  Xexclude from comparison	Google Cloud Datastore  Xexclude from comparison	JaguarDB  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	Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization	Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform	Performant, highly scalable DBMS for AI and IoT applications
Primary database model	Document store Key-value store	Time Series DBMS	Document store	Key-value store Vector DBMS
DB-Engines Ranking measures the popularity of database management systems Trend Chart		Score 0.35 Rank #282   Overall #25   Time Series DBMS	Score 4.36 Rank #72   Overall #12   Document stores	Score 0.06 Rank #381   Overall #59   Key-value stores #15   Vector DBMS
Website	atos.net/en/convergence-creators/portfolio/standard-common-repository	axibase.com/­docs/­atsd/­finance	cloud.google.com/­datastore	www.jaguardb.com
Technical documentation			cloud.google.com/­datastore/­docs	www.jaguardb.com/­support.html
Developer	Atos Convergence Creators	Axibase Corporation	Google	DataJaguar, Inc.
Initial release	2016	2013	2008	2015
Current release	1703	15585		3.3 July 2023
License Commercial or Open Source	commercial	commercial Community Edition (single node) is free, Enterprise Edition (distributed) is paid	commercial	Open Source GPL V3.0
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	Java		C++ the server part. Clients available in other languages
Server operating systems	Linux	Linux	hosted	Linux
Data scheme	Schema and schema-less with LDAP views	yes	schema-free	yes
Typing predefined data types such as float or date	optional	yes short, integer, long, float, double, decimal, string	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	no	yes	yes
SQL Support of SQL	no	SQL-like query language	SQL-like query language (GQL)	A subset of ANSI SQL is implemented but no views, foreign keys, triggers
APIs and other access methods	LDAP	JDBC Proprietary protocol (Network API) RESTful HTTP API	gRPC (using protocol buffers) API RESTful HTTP/JSON API	JDBC ODBC
Supported programming languages	All languages with LDAP bindings	Go Java PHP Python R Ruby	.Net Go Java JavaScript (Node.js) PHP Python Ruby	C C++ Go Java JavaScript (Node.js) PHP Python Ruby Scala
Server-side scripts Stored procedures	no	yes	using Google App Engine	no
Triggers	yes	yes	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	Source-replica replication	Multi-source replication using Paxos	Multi-source 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		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	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
User concepts Access control	LDAP bind authentication		Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)	rights management via user accounts
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More resources
	Atos Standard Common Repository	Axibase	Google Cloud Datastore	JaguarDB
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