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DBMS > 4D vs. Atos Standard Common Repository vs. Datomic vs. Splice Machine vs. Teradata Aster

System Properties Comparison 4D vs. Atos Standard Common Repository vs. Datomic vs. Splice Machine vs. Teradata Aster

Editorial information provided by DB-Engines
Name4D infoformer name: 4th Dimension  Xexclude from comparisonAtos Standard Common Repository  Xexclude from comparisonDatomic  Xexclude from comparisonSplice Machine  Xexclude from comparisonTeradata Aster  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.
DescriptionApplication development environment with integrated database management systemHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityOpen-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 types
Primary database modelRelational DBMSDocument store
Key-value store
Relational DBMSRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.58
Rank#108  Overall
#54  Relational DBMS
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitewww.4d.comatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.datomic.comsplicemachine.com
Technical documentationdeveloper.4d.comdocs.datomic.comsplicemachine.com/­how-it-works
Developer4D, IncAtos Convergence CreatorsCognitectSplice MachineTeradata
Initial release19842016201220142005
Current releasev20, April 202317031.0.6735, June 20233.1, March 2021
License infoCommercial or Open Sourcecommercialcommercialcommercial infolimited edition freeOpen Source infoAGPL 3.0, commercial license availablecommercial
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 languageJavaJava, ClojureJava
Server operating systemsOS X
Windows
LinuxAll OS with a Java VMLinux
OS X
Solaris
Windows
Linux
Data schemeyesSchema and schema-less with LDAP viewsyesyesFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
Typing infopredefined data types such as float or dateyesoptionalyesyesyes
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.yesyesnoyes infoin Aster File Store
Secondary indexesyesyesyesyesyes
SQL infoSupport of SQLyes infoclose to SQL 92nonoyesyes
APIs and other access methodsODBC
RESTful HTTP API infoby using 4D Mobile
SOAP webservices
LDAPRESTful HTTP APIJDBC
Native Spark Datasource
ODBC
ADO.NET
JDBC
ODBC
OLE DB
Supported programming languages4D proprietary IDE
PHP
All languages with LDAP bindingsClojure
Java
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresyesnoyes infoTransaction Functionsyes infoJavaR packages
TriggersyesyesBy using transaction functionsyesno
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infocell divisionnone infoBut extensive use of caching in the application peersShared Nothhing Auto-Sharding, Columnar PartitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationyesnone infoBut extensive use of caching in the application peersMulti-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.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoYes, via Full Spark Integrationyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyesnonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic execution of specific operationsACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes inforecommended only for testing and developmentyesno
User concepts infoAccess controlUsers and groupsLDAP bind authenticationnoAccess rights for users, groups and roles according to SQL-standardfine grained access rights according to SQL-standard

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More resources
4D infoformer name: 4th DimensionAtos Standard Common RepositoryDatomicSplice MachineTeradata Aster
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