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DBMS > FatDB vs. Greenplum vs. Spark SQL vs. Yaacomo

System Properties Comparison FatDB vs. Greenplum vs. Spark SQL vs. Yaacomo

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Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonGreenplum  Xexclude from comparisonSpark SQL  Xexclude from comparisonYaacomo  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
DescriptionA .NET NoSQL DBMS that can integrate with and extend SQL Server.Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database infoA logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.Spark SQL is a component on top of 'Spark Core' for structured data processingOpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelDocument store
Key-value store
Relational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score8.08
Rank#48  Overall
#31  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitegreenplum.orgspark.apache.org/­sqlyaacomo.com
Technical documentationdocs.greenplum.orgspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperFatCloudPivotal Software Inc.Apache Software FoundationQ2WEB GmbH
Initial release2012200520142009
Current release7.0.0, September 20233.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open 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
Android
Linux
Windows
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.yes infosince Version 4.2nono
Secondary indexesyesyesnoyes
SQL infoSupport of SQLno infoVia inetgration in SQL ServeryesSQL-like DML and DDL statementsyes
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
JDBC
ODBC
JDBC
ODBC
JDBC
ODBC
Supported programming languagesC#C
Java
Perl
Python
R
Java
Python
R
Scala
Server-side scripts infoStored proceduresyes infovia applicationsyesno
Triggersyes infovia applicationsyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Corehorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationnoneSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnoyes
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.nonoyes
User concepts infoAccess controlno infoCan implement custom security layer via applicationsfine grained access rights according to SQL-standardnofine grained access rights according to SQL-standard

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More resources
FatDBGreenplumSpark SQLYaacomo
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