DBMS > Atos Standard Common Repository vs. Google Cloud Bigtable vs. InfinityDB vs. RDF4J

System Properties Comparison Atos Standard Common Repository vs. Google Cloud Bigtable vs. InfinityDB vs. RDF4J

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Editorial information provided by DB-Engines
Name	Atos Standard Common Repository  Xexclude from comparison	Google Cloud Bigtable  Xexclude from comparison	InfinityDB  Xexclude from comparison	RDF4J formerly known as Sesame  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	Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.	A Java embedded Key-Value Store which extends the Java Map interface	RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.
Primary database model	Document store Key-value store	Key-value store Wide column store	Key-value store	RDF store
DB-Engines Ranking measures the popularity of database management systems Trend Chart		Score 3.15 Rank #95   Overall #14   Key-value stores #8   Wide column stores	Score 0.08 Rank #365   Overall #55   Key-value stores	Score 0.74 Rank #222   Overall #9   RDF stores
Website	atos.net/en/convergence-creators/portfolio/standard-common-repository	cloud.google.com/­bigtable	boilerbay.com	rdf4j.org
Technical documentation		cloud.google.com/­bigtable/­docs	boilerbay.com/­infinitydb/­manual	rdf4j.org/­documentation
Developer	Atos Convergence Creators	Google	Boiler Bay Inc.	Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release	2016	2015	2002	2004
Current release	1703		4.0
License Commercial or Open Source	commercial	commercial	commercial	Open Source Eclipse Distribution License (EDL), v1.0.
Cloud-based only Only available as a cloud service	no	yes	no	no
DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed.
Implementation language	Java		Java	Java
Server operating systems	Linux	hosted	All OS with a Java VM	Linux OS X Unix Windows
Data scheme	Schema and schema-less with LDAP views	schema-free	yes nested virtual Java Maps, multi-value, logical ‘tuple space’ runtime Schema upgrade	yes RDF Schemas
Typing predefined data types such as float or date	optional	no	yes all Java primitives, Date, CLOB, BLOB, huge sparse arrays	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	no	no manual creation possible, using inversions based on multi-value capability	yes
SQL Support of SQL	no	no	no	no
APIs and other access methods	LDAP	gRPC (using protocol buffers) API HappyBase (Python library) HBase compatible API (Java)	Access via java.util.concurrent.ConcurrentNavigableMap Interface Proprietary API to InfinityDB ItemSpace (boilerbay.com/­docs/­ItemSpaceDataStructures.htm)	Java API RIO RDF Input/Output Sail API SeRQL Sesame RDF Query Language Sesame REST HTTP Protocol SPARQL
Supported programming languages	All languages with LDAP bindings	C# C++ Go Java JavaScript (Node.js) Python	Java	Java PHP Python
Server-side scripts Stored procedures	no	no	no	yes
Triggers	yes	no	no	yes
Partitioning methods Methods for storing different data on different nodes	Sharding cell division	Sharding	none	none
Replication methods Methods for redundantly storing data on multiple nodes	yes	Internal replication in Colossus, and regional replication between two clusters in different zones	none	none
MapReduce Offers an API for user-defined Map/Reduce methods		yes	no	no
Consistency concepts Methods to ensure consistency in a distributed system	Immediate Consistency or Eventual Consistency depending on configuration	Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)	Immediate Consistency READ-COMMITTED or SERIALIZED
Foreign keys Referential integrity	no	no	no manual creation possible, using inversions based on multi-value capability
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data	Atomic execution of specific operations	Atomic single-row operations	ACID Optimistic locking for transactions; no isolation for bulk loads	ACID Isolation support depends on the API used
Concurrency Support for concurrent manipulation of data	yes	yes	yes	yes
Durability Support for making data persistent	yes	yes	yes	yes in-memory storage is supported as well
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)	no	no
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More resources
	Atos Standard Common Repository	Google Cloud Bigtable	InfinityDB	RDF4J formerly known as Sesame
Recent citations in the news	Infographic: What makes a Mobile Operator's setup future proof? 10 February 2024, Atos provided by Google News	Google says it'll stop charging fees to transfer data out of Google Cloud 11 January 2024, TechCrunch Google Introduces Autoscaling for Cloud Bigtable for Optimizing Costs 31 January 2022, InfoQ.com Google scales up Cloud Bigtable NoSQL database 27 January 2022, TechTarget Review: Google Bigtable scales with ease 7 September 2016, InfoWorld Google introduces Cloud Bigtable managed NoSQL database to process data at scale 6 May 2015, VentureBeat provided by Google News		GraphDB Goes Open Source 27 January 2020, iProgrammer Ontotext's GraphDB 8.10 Makes Knowledge Graph Experience Faster and Richer 13 June 2019, Markets Insider provided by Google News

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