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DBMS > FatDB vs. Spark SQL vs. Ultipa vs. Yanza

System Properties Comparison FatDB vs. Spark SQL vs. Ultipa vs. Yanza

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Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonUltipa  Xexclude from comparisonYanza  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionA .NET NoSQL DBMS that can integrate with and extend SQL Server.Spark SQL is a component on top of 'Spark Core' for structured data processingHigh performance Graph DBMS supporting HTAP high availability cluster deploymentTime Series DBMS for IoT Applications
Primary database modelDocument store
Key-value store
Relational DBMSGraph DBMSTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.19
Rank#330  Overall
#30  Graph DBMS
Websitespark.apache.org/­sqlwww.ultipa.comyanza.com
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.ultipa.com/­document
DeveloperFatCloudApache Software FoundationUltipaYanza
Initial release2012201420192015
Current release3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialcommercial infofree version available
Cloud-based only infoOnly available as a cloud servicenononono infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC#Scala
Server operating systemsWindowsLinux
OS X
Windows
Windows
Data schemeschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyesno
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.nono
Secondary indexesyesnono
SQL infoSupport of SQLno infoVia inetgration in SQL ServerSQL-like DML and DDL statementsno
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
JDBC
ODBC
RESTful HTTP APIHTTP API
Supported programming languagesC#Java
Python
R
Scala
C++
Go
Java
JavaScript (Node.js)
Python
any language that supports HTTP calls
Server-side scripts infoStored proceduresyes infovia applicationsnono
Triggersyes infovia applicationsnoyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonono
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.no
User concepts infoAccess controlno infoCan implement custom security layer via applicationsnono

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More resources
FatDBSpark SQLUltipaYanza
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