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DBMS > Atos Standard Common Repository vs. RDF4J vs. Splice Machine vs. Teradata Aster vs. Yaacomo

System Properties Comparison Atos Standard Common Repository vs. RDF4J vs. Splice Machine vs. Teradata Aster vs. Yaacomo

Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonSplice Machine  Xexclude from comparisonTeradata Aster  Xexclude from comparisonYaacomo  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
DescriptionHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkPlatform for big data analytics on multistructured data sources and typesOpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelDocument store
Key-value store
RDF storeRelational DBMSRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.74
Rank#222  Overall
#9  RDF stores
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositoryrdf4j.orgsplicemachine.comyaacomo.com
Technical documentationrdf4j.org/­documentationsplicemachine.com/­how-it-works
DeveloperAtos Convergence CreatorsSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.Splice MachineTeradataQ2WEB GmbH
Initial release20162004201420052009
Current release17033.1, March 2021
License infoCommercial or Open SourcecommercialOpen Source infoEclipse Distribution License (EDL), v1.0.Open Source infoAGPL 3.0, commercial license availablecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaJava
Server operating systemsLinuxLinux
OS X
Unix
Windows
Linux
OS X
Solaris
Windows
LinuxAndroid
Linux
Windows
Data schemeSchema and schema-less with LDAP viewsyes infoRDF SchemasyesFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Storeyes
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.yesyes infoin Aster File Storeno
Secondary indexesyesyesyesyesyes
SQL infoSupport of SQLnonoyesyesyes
APIs and other access methodsLDAPJava API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
JDBC
Native Spark Datasource
ODBC
ADO.NET
JDBC
ODBC
OLE DB
JDBC
ODBC
Supported programming languagesAll languages with LDAP bindingsJava
PHP
Python
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresnoyesyes infoJavaR packages
Triggersyesyesyesnoyes
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionnoneShared Nothhing Auto-Sharding, Columnar PartitioningShardinghorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesnoneMulti-source replication
Source-replica replication
yes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoYes, via Full Spark Integrationyes infoSQL Map-Reduce Frameworkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsACID infoIsolation support depends on the API usedACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)yesyes
Durability infoSupport for making data persistentyesyes infoin-memory storage is supported as wellyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnoyes
User concepts infoAccess controlLDAP bind authenticationnoAccess rights for users, groups and roles according to SQL-standardfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
Atos Standard Common RepositoryRDF4J infoformerly known as SesameSplice MachineTeradata AsterYaacomo
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