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DBMS > FatDB vs. OushuDB vs. Splice Machine

System Properties Comparison FatDB vs. OushuDB vs. Splice Machine

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Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonOushuDB  Xexclude from comparisonSplice Machine  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 learningOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelDocument store
Key-value store
Relational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.10
Rank#355  Overall
#154  Relational DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitewww.oushu.com/­product/­oushuDBsplicemachine.com
Technical documentationwww.oushu.com/­documentationsplicemachine.com/­how-it-works
DeveloperFatCloudOushuSplice Machine
Initial release20122014
Current release4.0.1, August 20203.1, March 2021
License infoCommercial or Open SourcecommercialcommercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC#Java
Server operating systemsWindowsLinuxLinux
OS X
Solaris
Windows
Data schemeschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyes
Secondary indexesyesyesyes
SQL infoSupport of SQLno infoVia inetgration in SQL ServerFull-featured ANSI SQL supportyes
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
JDBC
ODBC
JDBC
Native Spark Datasource
ODBC
Supported programming languagesC#C
C++
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyes infovia applicationsyesyes infoJava
Triggersyes infovia applicationsyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingyesShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesHadoop integrationYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)
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.yes
User concepts infoAccess controlno infoCan implement custom security layer via applicationsKerberos, SSL and role based accessAccess rights for users, groups and roles according to SQL-standard

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More resources
FatDBOushuDBSplice Machine
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