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DBMS > Atos Standard Common Repository vs. EXASOL vs. IRONdb vs. RDF4J vs. Splice Machine

System Properties Comparison Atos Standard Common Repository vs. EXASOL vs. IRONdb vs. RDF4J vs. Splice Machine

Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonEXASOL  Xexclude from comparisonIRONdb  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonSplice Machine  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksHigh-performance, in-memory, MPP database specifically designed for in-memory analytics.A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicityRDF4J 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 Spark
Primary database modelDocument store
Key-value store
Relational DBMSTime Series DBMSRDF storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.99
Rank#124  Overall
#58  Relational DBMS
Score0.69
Rank#230  Overall
#9  RDF stores
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.exasol.comwww.circonus.com/solutions/time-series-database/rdf4j.orgsplicemachine.com
Technical documentationwww.exasol.com/­resourcesdocs.circonus.com/irondb/category/getting-startedrdf4j.org/­documentationsplicemachine.com/­how-it-works
DeveloperAtos Convergence CreatorsExasolCirconus LLC.Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.Splice Machine
Initial release20162000201720042014
Current release1703V0.10.20, January 20183.1, March 2021
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoEclipse Distribution License (EDL), v1.0.Open Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC and C++JavaJava
Server operating systemsLinuxLinuxLinux
OS X
Unix
Windows
Linux
OS X
Solaris
Windows
Data schemeSchema and schema-less with LDAP viewsyesschema-freeyes infoRDF Schemasyes
Typing infopredefined data types such as float or dateoptionalyesyes infotext, numeric, histogramsyesyes
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.yesnono
Secondary indexesyesyesnoyesyes
SQL infoSupport of SQLnoyesSQL-like query language (Circonus Analytics Query Language: CAQL)noyes
APIs and other access methodsLDAP.Net
JDBC
ODBC
WebSocket
HTTP APIJava API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
JDBC
Native Spark Datasource
ODBC
Supported programming languagesAll languages with LDAP bindingsJava
Lua
Python
R
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
Java
PHP
Python
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnouser defined functionsyes, in Luayesyes infoJava
Triggersyesyesnoyesyes
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionShardingAutomatic, metric affinity per nodenoneShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesconfigurable replication factor, datacenter awarenoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoHadoop integrationnonoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate consistency per node, eventual consistency across nodesImmediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsACIDnoACID infoIsolation support depends on the API usedACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyes infoin-memory storage is supported as wellyes
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 authenticationAccess rights for users, groups and roles according to SQL-standardnonoAccess rights for users, groups and roles according to SQL-standard

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More resources
Atos Standard Common RepositoryEXASOLIRONdbRDF4J infoformerly known as SesameSplice Machine
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