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DBMS > Apache Phoenix vs. FatDB vs. InfluxDB vs. Microsoft Azure SQL Database vs. Spark SQL

System Properties Comparison Apache Phoenix vs. FatDB vs. InfluxDB vs. Microsoft Azure SQL Database vs. Spark SQL

Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonFatDB  Xexclude from comparisonInfluxDB  Xexclude from comparisonMicrosoft Azure SQL Database infoformerly SQL Azure  Xexclude from comparisonSpark SQL  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseA .NET NoSQL DBMS that can integrate with and extend SQL Server.DBMS for storing time series, events and metricsDatabase as a Service offering with high compatibility to Microsoft SQL ServerSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSDocument store
Key-value store
Time Series DBMSRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS infowith GEO packageDocument store
Graph DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score25.83
Rank#28  Overall
#1  Time Series DBMS
Score77.99
Rank#16  Overall
#11  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitephoenix.apache.orgwww.influxdata.com/­products/­influxdb-overviewazure.microsoft.com/­en-us/­products/­azure-sql/­databasespark.apache.org/­sql
Technical documentationphoenix.apache.orgdocs.influxdata.com/­influxdbdocs.microsoft.com/­en-us/­azure/­azure-sqlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationFatCloudMicrosoftApache Software Foundation
Initial release20142012201320102014
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20192.7.6, April 2024V123.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoMIT-License; commercial enterprise version availablecommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC#GoC++Scala
Server operating systemsLinux
Unix
Windows
WindowsLinux
OS X infothrough Homebrew
hostedLinux
OS X
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesNumeric data and Stringsyesyes
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.nonoyesno
Secondary indexesyesyesnoyesno
SQL infoSupport of SQLyesno infoVia inetgration in SQL ServerSQL-like query languageyesSQL-like DML and DDL statements
APIs and other access methodsJDBC.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
HTTP API
JSON over UDP
ADO.NET
JDBC
ODBC
JDBC
ODBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C#.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala
.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsyes infovia applicationsnoTransact SQLno
Triggersnoyes infovia applicationsnoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoin enterprise version onlyyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
selectable replication factorselectable replication factor infoin enterprise version onlyyes, with always 3 replicas availablenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infoDepending on used storage engineno
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyno infoCan implement custom security layer via applicationssimple rights management via user accountsfine grained access rights according to SQL-standardno
More information provided by the system vendor
Apache PhoenixFatDBInfluxDBMicrosoft Azure SQL Database infoformerly SQL AzureSpark SQL
Specific characteristicsInfluxData is the creator of InfluxDB , the open source time series database. It...
» more
Competitive advantagesTime to Value InfluxDB is available in all the popular languages and frameworks,...
» more
Typical application scenariosIoT & Sensor Monitoring Developers are witnessing the instrumentation of every available...
» more
Key customersInfluxData has more than 1,900 paying customers, including customers include MuleSoft,...
» more
Market metricsFastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances
» more
Licensing and pricing modelsOpen source core with closed source clustering available either on-premise or on...
» more
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