DBMS > Atos Standard Common Repository vs. Drizzle vs. EJDB vs. Google Cloud Bigtable

System Properties Comparison Atos Standard Common Repository vs. Drizzle vs. EJDB vs. Google Cloud Bigtable

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Editorial information provided by DB-Engines
Name	Atos Standard Common Repository  Xexclude from comparison	Drizzle  Xexclude from comparison	EJDB  Xexclude from comparison	Google Cloud Bigtable  Xexclude from comparison
	This system has been discontinued and will be removed from the DB-Engines ranking.	Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
Description	Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks	MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.	Embeddable document-store database library with JSON representation of queries (in MongoDB style)	Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.
Primary database model	Document store Key-value store	Relational DBMS	Document store	Key-value store Wide column store
DB-Engines Ranking measures the popularity of database management systems Trend Chart			Score 0.31 Rank #296   Overall #44   Document stores	Score 3.15 Rank #95   Overall #14   Key-value stores #8   Wide column stores
Website	atos.net/en/convergence-creators/portfolio/standard-common-repository		github.com/­Softmotions/­ejdb	cloud.google.com/­bigtable
Technical documentation			github.com/­Softmotions/­ejdb/­blob/­master/­README.md	cloud.google.com/­bigtable/­docs
Developer	Atos Convergence Creators	Drizzle project, originally started by Brian Aker	Softmotions	Google
Initial release	2016	2008	2012	2015
Current release	1703	7.2.4, September 2012
License Commercial or Open Source	commercial	Open Source GNU GPL	Open Source GPLv2	commercial
Cloud-based only Only available as a cloud service	no	no	no	yes
DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed.
Implementation language	Java	C++	C
Server operating systems	Linux	FreeBSD Linux OS X	server-less	hosted
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 string, integer, double, bool, date, object_id	no
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
Secondary indexes	yes	yes	no	no
SQL Support of SQL	no	yes with proprietary extensions	no	no
APIs and other access methods	LDAP	JDBC	in-process shared library	gRPC (using protocol buffers) API HappyBase (Python library) HBase compatible API (Java)
Supported programming languages	All languages with LDAP bindings	C C++ Java PHP	Actionscript C C# C++ Go Java JavaScript (Node.js) Lua Objective-C Pike Python Ruby	C# C++ Go Java JavaScript (Node.js) Python
Server-side scripts Stored procedures	no	no	no	no
Triggers	yes	no hooks for callbacks inside the server can be used.	no	no
Partitioning methods Methods for storing different data on different nodes	Sharding cell division	Sharding	none	Sharding
Replication methods Methods for redundantly storing data on multiple nodes	yes	Multi-source replication Source-replica replication	none	Internal replication in Colossus, and regional replication between two clusters in different zones
MapReduce Offers an API for user-defined Map/Reduce methods		no	no	yes
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)
Foreign keys Referential integrity	no	yes	no typically not needed, however similar functionality with collection joins possible	no
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data	Atomic execution of specific operations	ACID	no	Atomic single-row operations
Concurrency Support for concurrent manipulation of data	yes	yes	yes Read/Write Locking	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
User concepts Access control	LDAP bind authentication	Pluggable authentication mechanisms e.g. LDAP, HTTP	no	Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)
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More resources
	Atos Standard Common Repository	Drizzle	EJDB	Google Cloud Bigtable
DB-Engines blog posts		MySQL won the April ranking; did its forks follow? 1 April 2015, Paul Andlinger Has MySQL finally lost its mojo? 1 July 2013, Matthias Gelbmann show all
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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