DBMS > Atos Standard Common Repository vs. Faircom DB vs. InterSystems Caché vs. Newts

System Properties Comparison Atos Standard Common Repository vs. Faircom DB vs. InterSystems Caché vs. Newts

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Editorial information provided by DB-Engines
Name	Atos Standard Common Repository  Xexclude from comparison	Faircom DB formerly c-treeACE  Xexclude from comparison	InterSystems Caché  Xexclude from comparison	Newts  Xexclude from comparison
	This system has been discontinued and will be removed from the DB-Engines ranking.		Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.
Description	Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks	Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.	A multi-model DBMS and application server	Time Series DBMS based on Cassandra
Primary database model	Document store Key-value store	Key-value store Relational DBMS	Key-value store Object oriented DBMS Relational DBMS	Time Series DBMS
Secondary database models			Document store
DB-Engines Ranking measures the popularity of database management systems Trend Chart		Score 0.29 Rank #304   Overall #43   Key-value stores #136   Relational DBMS		Score 0.07 Rank #375   Overall #41   Time Series DBMS
Website	atos.net/en/convergence-creators/portfolio/standard-common-repository	www.faircom.com/­products/­faircom-db	www.intersystems.com/­products/­cache	opennms.github.io/­newts
Technical documentation		docs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.html	docs.intersystems.com	github.com/­OpenNMS/­newts/­wiki
Developer	Atos Convergence Creators	FairCom Corporation	InterSystems	OpenNMS Group
Initial release	2016	1979	1997	2014
Current release	1703	V12, November 2020	2018.1.4, May 2020
License Commercial or Open Source	commercial	commercial Restricted, free version available	commercial	Open Source Apache 2.0
Cloud-based only Only available as a cloud service	no	no	no	no
DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed.
Implementation language	Java	ANSI C, C++		Java
Server operating systems	Linux	AIX FreeBSD HP-UX Linux NetBSD OS X QNX SCO Solaris VxWorks Windows easily portable to other OSs	AIX HP Open VMS HP-UX Linux OS X Solaris Windows	Linux OS X Windows
Data scheme	Schema and schema-less with LDAP views	schema free, schema optional, schema required, partial schema,	depending on used data model	schema-free
Typing predefined data types such as float or date	optional	yes, ANSI SQL Types, JSON, typed binary structures	yes	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	yes	no
Secondary indexes	yes	yes	yes	no
SQL Support of SQL	no	yes, ANSI SQL with proprietary extensions	yes	no
APIs and other access methods	LDAP	ADO.NET Direct SQL JDBC JPA ODBC RESTful HTTP/JSON API RESTful MQTT/JSON API RPC	.NET Client API JDBC ODBC RESTful HTTP API	HTTP REST Java API
Supported programming languages	All languages with LDAP bindings	.Net C C# C++ Java JavaScript (Node.js and browser) PHP Python Visual Basic	C# C++ Java	Java
Server-side scripts Stored procedures	no	yes .Net, JavaScript, C/C++	yes	no
Triggers	yes	yes	yes	no
Partitioning methods Methods for storing different data on different nodes	Sharding cell division	File partitioning, horizontal partitioning, sharding Customizable business rules for table partitioning	none	Sharding based on Cassandra
Replication methods Methods for redundantly storing data on multiple nodes	yes	yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).	Source-replica replication	selectable replication factor based on Cassandra
MapReduce Offers an API for user-defined Map/Reduce methods		no	no	no
Consistency concepts Methods to ensure consistency in a distributed system	Immediate Consistency or Eventual Consistency depending on configuration	Eventual Consistency Immediate Consistency Tunable consistency per server, database, table, and transaction	Immediate Consistency	Eventual Consistency based on Cassandra Immediate Consistency based on Cassandra
Foreign keys Referential integrity	no	yes	yes	no
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data	Atomic execution of specific operations	tunable from ACID to Eventually Consistent	ACID	no
Concurrency Support for concurrent manipulation of data	yes	yes	yes	yes
Durability Support for making data persistent	yes	Yes, tunable from durable to delayed durability to in-memory	yes	yes
In-memory capabilities Is there an option to define some or all structures to be held in-memory only.	yes	yes	yes	no
User concepts Access control	LDAP bind authentication	Fine grained access rights according to SQL-standard with additional protections for files	Access rights for users, groups and roles	no
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More resources
	Atos Standard Common Repository	Faircom DB formerly c-treeACE	InterSystems Caché	Newts
Recent citations in the news	Infographic: What makes a Mobile Operator's setup future proof? 10 February 2024, Atos provided by Google News	FairCom Unveils New Look, FairCom DB v13: Introducing 'DB Made Simple' 4 June 2024, Business Wire FairCom kicks off new era of database technology USA - English 10 November 2020, PR Newswire provided by Google News	AWS, GCP, Oracle, Azure, SAP Lead Cloud DBMS Market: Gartner 12 February 2022, CRN Announcing IBM Spectrum Sentinel: Building a Cyber Resilient Future 24 June 2022, IBM Associative Data Modeling Demystified - Part1 - DataScienceCentral.com 9 July 2016, Data Science Central Choosing a Database Technology. A roadmap and process overview | by Shirish Joshi 23 February 2020, Towards Data Science Nearly three years on from Cambridge's Epic go-live 23 August 2017, Digital Health provided by Google News

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