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DBMS > BigObject vs. HEAVY.AI vs. MonetDB

System Properties Comparison BigObject vs. HEAVY.AI vs. MonetDB

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Editorial information provided by DB-Engines
NameBigObject  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonMonetDB  Xexclude from comparison
DescriptionAnalytic DBMS for real-time computations and queriesA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareA relational database management system that stores data in columns
Primary database modelRelational DBMS infoa hierachical model (tree) can be imposedRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMSDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.13
Rank#332  Overall
#147  Relational DBMS
Score1.52
Rank#147  Overall
#67  Relational DBMS
Score1.70
Rank#137  Overall
#63  Relational DBMS
Websitebigobject.iogithub.com/­heavyai/­heavydb
www.heavy.ai
www.monetdb.org
Technical documentationdocs.bigobject.iodocs.heavy.aiwww.monetdb.org/­Documentation
DeveloperBigObject, Inc.HEAVY.AI, Inc.MonetDB BV
Initial release201520162004
Current release5.10, January 2022Dec2023 (11.49), December 2023
License infoCommercial or Open Sourcecommercial infofree community edition availableOpen Source infoApache Version 2; enterprise edition availableOpen Source infoMozilla Public License 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++ and CUDAC
Server operating systemsLinux infodistributed as a docker-image
OS X infodistributed as a docker-image (boot2docker)
Windows infodistributed as a docker-image (boot2docker)
LinuxFreeBSD
Linux
OS X
Solaris
Windows
Data schemeyesyesyes
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.nono
Secondary indexesyesnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyesyes infoSQL 2003 with some extensions
APIs and other access methodsfluentd
ODBC
RESTful HTTP API
JDBC
ODBC
Thrift
Vega
JDBC
native C library infoMAPI library (MonetDB application programming interface)
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC/Thrift
Python
C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Server-side scripts infoStored proceduresLuanoyes, in SQL, C, R
Triggersnonoyes
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infoRound robinSharding via remote tables
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replicationnone infoSource-replica replication available in experimental status
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate Consistency
Foreign keys infoReferential integrityyes infoautomatically between fact table and dimension tablesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
Concurrency infoSupport for concurrent manipulation of datayes infoRead/write lock on objects (tables, trees)yesyes
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.yesyes
User concepts infoAccess controlnofine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
BigObjectHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022MonetDB
Recent citations in the news

HEAVY.AI Launches HEAVY 7.0, Introducing Real-Time Machine Learning Capabilities
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