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DBMS > ArcadeDB vs. Atos Standard Common Repository vs. Databricks vs. NSDb vs. RDF4J

System Properties Comparison ArcadeDB vs. Atos Standard Common Repository vs. Databricks vs. NSDb vs. RDF4J

Editorial information provided by DB-Engines
NameArcadeDB  Xexclude from comparisonAtos Standard Common Repository  Xexclude from comparisonDatabricks  Xexclude from comparisonNSDb  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.
DescriptionFast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewrittenHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.
Primary database modelDocument store
Graph DBMS
Key-value store
Time Series DBMS infoin next version
Document store
Key-value store
Document store
Relational DBMS
Time Series DBMSRDF store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.02
Rank#366  Overall
#50  Document stores
#38  Graph DBMS
#53  Key-value stores
#36  Time Series DBMS
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.00
Rank#383  Overall
#41  Time Series DBMS
Score0.69
Rank#230  Overall
#9  RDF stores
Websitearcadedb.comatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.databricks.comnsdb.iordf4j.org
Technical documentationdocs.arcadedb.comdocs.databricks.comnsdb.io/­Architecturerdf4j.org/­documentation
DeveloperArcade DataAtos Convergence CreatorsDatabricksSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release20212016201320172004
Current releaseSeptember 20211703
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infoApache Version 2.0Open Source infoEclipse Distribution License (EDL), v1.0.
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaJava, ScalaJava
Server operating systemsAll OS with a Java VMLinuxhostedLinux
macOS
Linux
OS X
Unix
Windows
Data schemeschema-freeSchema and schema-less with LDAP viewsFlexible Schema (defined schema, partial schema, schema free)yes infoRDF Schemas
Typing infopredefined data types such as float or dateyesoptionalyes: int, bigint, decimal, stringyes
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.noyesyesno
Secondary indexesyesyesyesall fields are automatically indexedyes
SQL infoSupport of SQLSQL-like query language, no joinsnowith Databricks SQLSQL-like query languageno
APIs and other access methodsJDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin
LDAPJDBC
ODBC
RESTful HTTP API
gRPC
HTTP REST
WebSocket
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Supported programming languagesJavaAll languages with LDAP bindingsPython
R
Scala
Java
Scala
Java
PHP
Python
Server-side scripts infoStored proceduresnouser defined functions and aggregatesnoyes
Triggersyesyes
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyesyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyes inforelationship in graphsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic execution of specific operationsACIDnoACID infoIsolation support depends on the API used
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesUsing Apache Luceneyes infoin-memory storage is supported as well
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesno
User concepts infoAccess controlLDAP bind authenticationno
More information provided by the system vendor
ArcadeDBAtos Standard Common RepositoryDatabricksNSDbRDF4J infoformerly known as Sesame
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
ArcadeDBAtos Standard Common RepositoryDatabricksNSDbRDF4J infoformerly known as Sesame
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