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

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

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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	Yaacomo  Xexclude from comparison
	This system has been discontinued and will be removed from the DB-Engines ranking.			Yaacomo seems to be discontinued and is 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	OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database model	Document store Key-value store	Key-value store Wide column store	Key-value store	Relational DBMS
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
Website	atos.net/en/convergence-creators/portfolio/standard-common-repository	cloud.google.com/­bigtable	boilerbay.com	yaacomo.com
Technical documentation		cloud.google.com/­bigtable/­docs	boilerbay.com/­infinitydb/­manual
Developer	Atos Convergence Creators	Google	Boiler Bay Inc.	Q2WEB GmbH
Initial release	2016	2015	2002	2009
Current release	1703		4.0
License Commercial or Open Source	commercial	commercial	commercial	commercial
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
Server operating systems	Linux	hosted	All OS with a Java VM	Android Linux 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
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	no
Secondary indexes	yes	no	no manual creation possible, using inversions based on multi-value capability	yes
SQL Support of SQL	no	no	no	yes
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)	JDBC ODBC
Supported programming languages	All languages with LDAP bindings	C# C++ Go Java JavaScript (Node.js) Python	Java
Server-side scripts Stored procedures	no	no	no
Triggers	yes	no	no	yes
Partitioning methods Methods for storing different data on different nodes	Sharding cell division	Sharding	none	horizontal partitioning
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	Source-replica replication
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	Immediate Consistency
Foreign keys Referential integrity	no	no	no manual creation possible, using inversions based on multi-value capability	yes
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
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	Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)	no	fine grained access rights according to SQL-standard
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More resources
	Atos Standard Common Repository	Google Cloud Bigtable	InfinityDB	Yaacomo
Recent citations in the news		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 Cloud makes it cheaper to run smaller workloads on Bigtable 7 April 2020, TechCrunch Google introduces Cloud Bigtable managed NoSQL database to process data at scale 6 May 2015, VentureBeat provided by Google News

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