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DBMS > FatDB vs. Heroic vs. Splice Machine

System Properties Comparison FatDB vs. Heroic vs. Splice Machine

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Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonHeroic  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.Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchOpen-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
Time Series DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.57
Rank#250  Overall
#21  Time Series DBMS
Score0.54
Rank#255  Overall
#116  Relational DBMS
Websitegithub.com/­spotify/­heroicsplicemachine.com
Technical documentationspotify.github.io/­heroicsplicemachine.com/­how-it-works
DeveloperFatCloudSpotifySplice Machine
Initial release201220142014
Current release3.1, March 2021
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open 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#JavaJava
Server operating systemsWindowsLinux
OS X
Solaris
Windows
Data schemeschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyesyes
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
Secondary indexesyesyes infovia Elasticsearchyes
SQL infoSupport of SQLno infoVia inetgration in SQL Servernoyes
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
HQL (Heroic Query Language, a JSON-based language)
HTTP API
JDBC
Native Spark Datasource
ODBC
Supported programming languagesC#C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyes infovia applicationsnoyes infoJava
Triggersyes infovia applicationsnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
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.noyes
User concepts infoAccess controlno infoCan implement custom security layer via applicationsAccess rights for users, groups and roles according to SQL-standard

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More resources
FatDBHeroicSplice Machine
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