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DBMS > BigObject vs. FatDB vs. OrigoDB vs. Spark SQL

System Properties Comparison BigObject vs. FatDB vs. OrigoDB vs. Spark SQL

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Editorial information provided by DB-Engines
NameBigObject  Xexclude from comparisonFatDB  Xexclude from comparisonOrigoDB  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.
DescriptionAnalytic DBMS for real-time computations and queriesA .NET NoSQL DBMS that can integrate with and extend SQL Server.A fully ACID in-memory object graph databaseSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMS infoa hierachical model (tree) can be imposedDocument store
Key-value store
Document store
Object oriented DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.19
Rank#329  Overall
#146  Relational DBMS
Score0.06
Rank#380  Overall
#50  Document stores
#18  Object oriented DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitebigobject.ioorigodb.comspark.apache.org/­sql
Technical documentationdocs.bigobject.ioorigodb.com/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperBigObject, Inc.FatCloudRobert Friberg et alApache Software Foundation
Initial release201520122009 infounder the name LiveDB2014
Current release3.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercial infofree community edition availablecommercialOpen SourceOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC#C#Scala
Server operating systemsLinux infodistributed as a docker-image
OS X infodistributed as a docker-image (boot2docker)
Windows infodistributed as a docker-image (boot2docker)
WindowsLinux
Windows
Linux
OS X
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesUser defined using .NET types and collectionsyes
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 infocan be achieved using .NETno
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsno infoVia inetgration in SQL ServernoSQL-like DML and DDL statements
APIs and other access methodsfluentd
ODBC
RESTful HTTP API
.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
.NET Client API
HTTP API
LINQ
JDBC
ODBC
Supported programming languagesC#.NetJava
Python
R
Scala
Server-side scripts infoStored proceduresLuayes infovia applicationsyesno
Triggersnoyes infovia applicationsyes infoDomain Eventsno
Partitioning methods infoMethods for storing different data on different nodesnoneShardinghorizontal partitioning infoclient side managed; servers are not synchronizedyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesnoneselectable replication factorSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyes infoautomatically between fact table and dimension tablesnodepending on modelno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDno
Concurrency infoSupport for concurrent manipulation of datayes infoRead/write lock on objects (tables, trees)yesyesyes
Durability infoSupport for making data persistentyesyesyes infoWrite ahead logyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
User concepts infoAccess controlnono infoCan implement custom security layer via applicationsRole based authorizationno

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More resources
BigObjectFatDBOrigoDBSpark SQL
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