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DBMS > EsgynDB vs. FatDB vs. Microsoft Azure SQL Database vs. Spark SQL vs. TimescaleDB

System Properties Comparison EsgynDB vs. FatDB vs. Microsoft Azure SQL Database vs. Spark SQL vs. TimescaleDB

Editorial information provided by DB-Engines
NameEsgynDB  Xexclude from comparisonFatDB  Xexclude from comparisonMicrosoft Azure SQL Database infoformerly SQL Azure  Xexclude from comparisonSpark SQL  Xexclude from comparisonTimescaleDB  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionA .NET NoSQL DBMS that can integrate with and extend SQL Server.Database as a Service offering with high compatibility to Microsoft SQL ServerSpark SQL is a component on top of 'Spark Core' for structured data processingA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMSDocument store
Key-value store
Relational DBMSRelational DBMSTime Series DBMS
Secondary database modelsDocument store
Graph DBMS
Spatial DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score77.99
Rank#16  Overall
#11  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitewww.esgyn.cnazure.microsoft.com/­en-us/­products/­azure-sql/­databasespark.apache.org/­sqlwww.timescale.com
Technical documentationdocs.microsoft.com/­en-us/­azure/­azure-sqlspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.timescale.com
DeveloperEsgynFatCloudMicrosoftApache Software FoundationTimescale
Initial release20152012201020142017
Current releaseV123.5.0 ( 2.13), September 20232.13.0, November 2023
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoApache 2.0Open Source infoApache 2.0
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 languageC++, JavaC#C++ScalaC
Server operating systemsLinuxWindowshostedLinux
OS X
Windows
Linux
OS X
Windows
Data schemeyesschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.noyesnoyes
Secondary indexesyesyesyesnoyes
SQL infoSupport of SQLyesno infoVia inetgration in SQL ServeryesSQL-like DML and DDL statementsyes infofull PostgreSQL SQL syntax
APIs and other access methodsADO.NET
JDBC
ODBC
.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
ADO.NET
JDBC
ODBC
JDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesAll languages supporting JDBC/ODBC/ADO.NetC#.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresJava Stored Proceduresyes infovia applicationsTransact SQLnouser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnoyes infovia applicationsyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Coreyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication between multi datacentersselectable replication factoryes, with always 3 replicas availablenoneSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDnoACID
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.nonono
User concepts infoAccess controlfine grained access rights according to SQL-standardno infoCan implement custom security layer via applicationsfine grained access rights according to SQL-standardnofine grained access rights according to SQL-standard

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More resources
EsgynDBFatDBMicrosoft Azure SQL Database infoformerly SQL AzureSpark SQLTimescaleDB
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