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DBMS > FatDB vs. OushuDB vs. Spark SQL vs. Teradata

System Properties Comparison FatDB vs. OushuDB vs. Spark SQL vs. Teradata

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Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonOushuDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonTeradata  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.A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learningSpark SQL is a component on top of 'Spark Core' for structured data processingA hybrid cloud data analytics software platform (Teradata Vantage)
Primary database modelDocument store
Key-value store
Relational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument store
Graph DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.10
Rank#355  Overall
#154  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score44.87
Rank#22  Overall
#15  Relational DBMS
Websitewww.oushu.com/­product/­oushuDBspark.apache.org/­sqlwww.teradata.com
Technical documentationwww.oushu.com/­documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.teradata.com
DeveloperFatCloudOushuApache Software FoundationTeradata
Initial release201220141984
Current release4.0.1, August 20203.5.0 ( 2.13), September 2023Teradata Vantage 1.0 MU2, January 2019
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0commercial
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#Scala
Server operating systemsWindowsLinuxLinux
OS X
Windows
hosted
Linux
Data schemeschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.noyes
Secondary indexesyesyesnoyes infoJoin-index to prejoin tables, aggregate index, sparse index, hash index
SQL infoSupport of SQLno infoVia inetgration in SQL ServerFull-featured ANSI SQL supportSQL-like DML and DDL statementsyes infoSQL 2016 + extensions
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
JDBC
ODBC
JDBC
ODBC
.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB
Supported programming languagesC#C
C++
Java
Python
R
Scala
C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby
Server-side scripts infoStored proceduresyes infovia applicationsyesnoyes infoUDFs, stored procedures, table functions in parallel
Triggersyes infovia applicationsyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingyesyes, utilizing Spark CoreSharding infoHashing
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesHadoop integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlno infoCan implement custom security layer via applicationsKerberos, SSL and role based accessnofine grained access rights according to SQL-standard

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More resources
FatDBOushuDBSpark SQLTeradata
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