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DBMS > Apache Phoenix vs. Atos Standard Common Repository vs. Databricks vs. QuestDB vs. RDF4J

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

Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonAtos Standard Common Repository  Xexclude from comparisonDatabricks  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.
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseHighly 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.A 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 modelRelational DBMSDocument store
Key-value store
Document store
Relational DBMS
Time Series DBMSRDF store
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.02
Rank#130  Overall
#63  Relational DBMS
Score76.33
Rank#17  Overall
#3  Document stores
#11  Relational DBMS
Score2.48
Rank#115  Overall
#9  Time Series DBMS
Score0.71
Rank#231  Overall
#9  RDF stores
Websitephoenix.apache.orgatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.databricks.comquestdb.iordf4j.org
Technical documentationphoenix.apache.orgdocs.databricks.comquestdb.io/­docsrdf4j.org/­documentation
DeveloperApache Software FoundationAtos Convergence CreatorsDatabricksQuestDB Technology IncSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release20142016201320142004
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20191703
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infoApache 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

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaJava (Zero-GC), C++, RustJava
Server operating systemsLinux
Unix
Windows
LinuxhostedLinux
macOS
Windows
Linux
OS X
Unix
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesSchema and schema-less with LDAP viewsFlexible Schema (defined schema, partial schema, schema free)yes infoschema-free via InfluxDB Line Protocolyes infoRDF Schemas
Typing infopredefined data types such as float or dateyesoptionalyesyes
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 indexesyesyesyesnoyes
SQL infoSupport of SQLyesnowith Databricks SQLSQL with time-series extensionsno
APIs and other access methodsJDBCLDAPJDBC
ODBC
RESTful HTTP API
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 languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
All languages with LDAP bindingsPython
R
Scala
C infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
Java
PHP
Python
Server-side scripts infoStored proceduresuser defined functionsnouser defined functions and aggregatesnoyes
Triggersnoyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infocell divisionhorizontal partitioning (by timestamps)none
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
yesyesSource-replica replication with eventual consistencynone
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic execution of specific operationsACIDACID 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.yesyesnoyes infothrough memory mapped files
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyLDAP bind authenticationno
More information provided by the system vendor
Apache PhoenixAtos Standard Common RepositoryDatabricksQuestDBRDF4J 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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