DBMS > Atos Standard Common Repository vs. Google Cloud Bigtable vs. Postgres-XL vs. Sadas Engine

System Properties Comparison Atos Standard Common Repository vs. Google Cloud Bigtable vs. Postgres-XL vs. Sadas Engine

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Editorial information provided by DB-Engines
Name	Atos Standard Common Repository  Xexclude from comparison	Google Cloud Bigtable  Xexclude from comparison	Postgres-XL  Xexclude from comparison	Sadas Engine  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.	Based on PostgreSQL enhanced with MPP and write-scale-out cluster features	SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments
Primary database model	Document store Key-value store	Key-value store Wide column store	Relational DBMS	Relational DBMS
Secondary database models			Document store Spatial 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 0.53 Rank #254   Overall #117   Relational DBMS	Score 0.07 Rank #373   Overall #157   Relational DBMS
Website	atos.net/en/convergence-creators/portfolio/standard-common-repository	cloud.google.com/­bigtable	www.postgres-xl.org	www.sadasengine.com
Technical documentation		cloud.google.com/­bigtable/­docs	www.postgres-xl.org/­documentation	www.sadasengine.com/­en/­sadas-engine-download-free-trial-and-documentation/­#documentation
Developer	Atos Convergence Creators	Google		SADAS s.r.l.
Initial release	2016	2015	2014 since 2012, originally named StormDB	2006
Current release	1703		10 R1, October 2018	8.0
License Commercial or Open Source	commercial	commercial	Open Source Mozilla public license	commercial free trial version available
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		C	C++
Server operating systems	Linux	hosted	Linux macOS	AIX Linux Windows
Data scheme	Schema and schema-less with LDAP views	schema-free	yes	yes
Typing predefined data types such as float or date	optional	no	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 XML type, but no XML query functionality	no
Secondary indexes	yes	no	yes	yes
SQL Support of SQL	no	no	yes distributed, parallel query execution	yes
APIs and other access methods	LDAP	gRPC (using protocol buffers) API HappyBase (Python library) HBase compatible API (Java)	ADO.NET JDBC native C library ODBC streaming API for large objects	JDBC ODBC Proprietary protocol
Supported programming languages	All languages with LDAP bindings	C# C++ Go Java JavaScript (Node.js) Python	.Net C C++ Delphi Erlang Java JavaScript (Node.js) Perl PHP Python Tcl	.Net C C# C++ Groovy Java PHP Python
Server-side scripts Stored procedures	no	no	user defined functions	no
Triggers	yes	no	yes	no
Partitioning methods Methods for storing different data on different nodes	Sharding cell division	Sharding	horizontal partitioning	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
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	Immediate Consistency
Foreign keys Referential integrity	no	no	yes	yes
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data	Atomic execution of specific operations	Atomic single-row operations	ACID MVCC
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 managed by 'Learn by Usage'
User concepts Access control	LDAP bind authentication	Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)	fine grained access rights according to SQL-standard	Access rights for users, groups and roles according to SQL-standard
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More resources
	Atos Standard Common Repository	Google Cloud Bigtable	Postgres-XL	Sadas Engine
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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