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DBMS > Atos Standard Common Repository vs. Databricks vs. HBase vs. QuestDB vs. RDF4J

System Properties Comparison Atos Standard Common Repository vs. Databricks vs. HBase vs. QuestDB vs. RDF4J

Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonDatabricks  Xexclude from comparisonHBase  Xexclude from comparisonQuestDB  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.
DescriptionHighly 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.Wide-column store based on Apache Hadoop and on concepts of BigTableA high performance open source SQL database for time series dataRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.
Primary database modelDocument store
Key-value store
Document store
Relational DBMS
Wide column storeTime Series DBMSRDF store
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score76.33
Rank#17  Overall
#3  Document stores
#11  Relational DBMS
Score31.25
Rank#26  Overall
#2  Wide column stores
Score2.48
Rank#115  Overall
#9  Time Series DBMS
Score0.71
Rank#231  Overall
#9  RDF stores
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.databricks.comhbase.apache.orgquestdb.iordf4j.org
Technical documentationdocs.databricks.comhbase.apache.org/­book.htmlquestdb.io/­docsrdf4j.org/­documentation
DeveloperAtos Convergence CreatorsDatabricksApache Software Foundation infoApache top-level project, originally developed by PowersetQuestDB Technology IncSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release20162013200820142004
Current release17032.3.4, January 2021
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache version 2Open Source infoApache 2.0Open Source infoEclipse Distribution License (EDL), v1.0.
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaJava (Zero-GC), C++, RustJava
Server operating systemsLinuxhostedLinux
Unix
Windows infousing Cygwin
Linux
macOS
Windows
Linux
OS X
Unix
Windows
Data schemeSchema and schema-less with LDAP viewsFlexible Schema (defined schema, partial schema, schema free)schema-free, schema definition possibleyes infoschema-free via InfluxDB Line Protocolyes infoRDF Schemas
Typing infopredefined data types such as float or dateoptionaloptions to bring your own types, AVROyesyes
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.yesyesnono
Secondary indexesyesyesnonoyes
SQL infoSupport of SQLnowith Databricks SQLnoSQL with time-series extensionsno
APIs and other access methodsLDAPJDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP API
Thrift
HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Supported programming languagesAll languages with LDAP bindingsPython
R
Scala
C
C#
C++
Groovy
Java
PHP
Python
Scala
C infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
Java
PHP
Python
Server-side scripts infoStored proceduresnouser defined functions and aggregatesyes infoCoprocessors in Javanoyes
Triggersyesyesnoyes
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionShardinghorizontal partitioning (by timestamps)none
Replication methods infoMethods for redundantly storing data on multiple nodesyesyesMulti-source replication
Source-replica replication
Source-replica replication with eventual consistencynone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency or Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsACIDSingle row ACID (across millions of columns)ACID for single-table writesACID infoIsolation support depends on the API used
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes 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.yesnoyesyes infothrough memory mapped files
User concepts infoAccess controlLDAP bind authenticationAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACno
More information provided by the system vendor
Atos Standard Common RepositoryDatabricksHBaseQuestDBRDF4J infoformerly known as Sesame
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
» more
Relational model with native time series support Column-based storage and time partitioned...
» more
Competitive advantagesHigh ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more
Typical application scenariosFinancial tick data Industrial IoT Application Metrics Monitoring
» more
Key customersBanks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more
Licensing and pricing modelsOpen source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more
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