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DBMS > Atos Standard Common Repository vs. FatDB vs. GigaSpaces vs. Google BigQuery vs. Microsoft Access

System Properties Comparison Atos Standard Common Repository vs. FatDB vs. GigaSpaces vs. Google BigQuery vs. Microsoft Access

Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonFatDB  Xexclude from comparisonGigaSpaces  Xexclude from comparisonGoogle BigQuery  Xexclude from comparisonMicrosoft Access  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksA .NET NoSQL DBMS that can integrate with and extend SQL Server.High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented TransactionsLarge scale data warehouse service with append-only tablesMicrosoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries. infoThe Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)
Primary database modelDocument store
Key-value store
Document store
Key-value store
Document store
Object oriented DBMS infoValues are user defined objects
Relational DBMSRelational DBMS
Secondary database modelsGraph DBMS
Search engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.97
Rank#192  Overall
#32  Document stores
#6  Object oriented DBMS
Score60.38
Rank#19  Overall
#13  Relational DBMS
Score104.92
Rank#11  Overall
#8  Relational DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.gigaspaces.comcloud.google.com/­bigquerywww.microsoft.com/­en-us/­microsoft-365/­access
Technical documentationdocs.gigaspaces.com/­latest/­landing.htmlcloud.google.com/­bigquery/­docsdeveloper.microsoft.com/­en-us/­access
DeveloperAtos Convergence CreatorsFatCloudGigaspaces TechnologiesGoogleMicrosoft
Initial release20162012200020101992
Current release170315.5, September 20201902 (16.0.11328.20222), March 2019
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache Version 2; Commercial licenses availablecommercialcommercial infoBundled with Microsoft Office
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC#Java, C++, .NetC++
Server operating systemsLinuxWindowsLinux
macOS
Solaris
Windows
hostedWindows infoNot a real database server, but making use of DLLs
Data schemeSchema and schema-less with LDAP viewsschema-freeschema-freeyesyes
Typing infopredefined data types such as float or dateoptionalyesyesyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.yesno infoXML can be used for describing objects metadatano
Secondary indexesyesyesyesnoyes
SQL infoSupport of SQLnono infoVia inetgration in SQL ServerSQL-99 for query and DML statementsyesyes infobut not compliant to any SQL standard
APIs and other access methodsLDAP.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
RESTful HTTP/JSON APIADO.NET
DAO
ODBC
OLE DB
Supported programming languagesAll languages with LDAP bindingsC#.Net
C++
Java
Python
Scala
.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
Server-side scripts infoStored proceduresnoyes infovia applicationsyesuser defined functions infoin JavaScriptyes infosince Access 2010 using the ACE-engine
Triggersyesyes infovia applicationsyes, event driven architecturenoyes infosince Access 2010 using the ACE-engine
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionShardingShardingnonenone
Replication methods infoMethods for redundantly storing data on multiple nodesyesselectable replication factorMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes infoMap-Reduce pattern can be built with XAP task executorsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationEventual Consistency
Immediate Consistency
Immediate Consistency infoConsistency level configurable: ALL, QUORUM, ANYImmediate Consistency
Foreign keys infoReferential integritynonononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsnoACIDno infoSince BigQuery is designed for querying dataACID infobut no files for transaction logging
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes infobut no files for transaction logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
User concepts infoAccess controlLDAP bind authenticationno infoCan implement custom security layer via applicationsRole-based access controlAccess privileges (owner, writer, reader) on dataset, table or view level infoGoogle Cloud Identity & Access Management (IAM)no infoa simple user-level security was built in till version Access 2003

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More resources
Atos Standard Common RepositoryFatDBGigaSpacesGoogle BigQueryMicrosoft Access
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