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DBMS > FatDB vs. Ingres vs. NSDb vs. Spark SQL

System Properties Comparison FatDB vs. Ingres vs. NSDb vs. Spark SQL

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Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonIngres  Xexclude from comparisonNSDb  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 .NET NoSQL DBMS that can integrate with and extend SQL Server.Well established RDBMSScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument store
Key-value store
Relational DBMSTime Series DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score5.15
Rank#74  Overall
#40  Relational DBMS
Score0.07
Rank#369  Overall
#40  Time Series DBMS
Score19.56
Rank#34  Overall
#21  Relational DBMS
Websitewww.actian.com/­databases/­ingresnsdb.iospark.apache.org/­sql
Technical documentationdocs.actian.com/­ingresnsdb.io/­Architecturespark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperFatCloudActian CorporationApache Software Foundation
Initial release20121974 infooriginally developed at University Berkely in early 1970s20172014
Current release11.2, May 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache Version 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC#CJava, ScalaScala
Server operating systemsWindowsAIX
HP Open VMS
HP-UX
Linux
Solaris
Windows
Linux
macOS
Linux
OS X
Windows
Data schemeschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyes: int, bigint, decimal, stringyes
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.no infobut tools for importing/exporting data from/to XML-files availablenono
Secondary indexesyesyesall fields are automatically indexedno
SQL infoSupport of SQLno infoVia inetgration in SQL ServeryesSQL-like query languageSQL-like DML and DDL statements
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)
gRPC
HTTP REST
WebSocket
JDBC
ODBC
Supported programming languagesC#Java
Scala
Java
Python
R
Scala
Server-side scripts infoStored proceduresyes infovia applicationsyesnono
Triggersyes infovia applicationsyesno
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning infoIngres Star to access multiple databases simultaneouslyShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorIngres Replicatornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyes infoMVCCyesyes
Durability infoSupport for making data persistentyesyesUsing Apache Luceneyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlno infoCan implement custom security layer via applicationsfine grained access rights according to SQL-standardno

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More resources
FatDBIngresNSDbSpark SQL
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