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DBMS > BigchainDB vs. dBASE vs. GridDB vs. HEAVY.AI vs. Hive

System Properties Comparison BigchainDB vs. dBASE vs. GridDB vs. HEAVY.AI vs. Hive

Editorial information provided by DB-Engines
NameBigchainDB  Xexclude from comparisondBASE  Xexclude from comparisonGridDB  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonHive  Xexclude from comparison
DescriptionBigchainDB is scalable blockchain database offering decentralization, immutability and native assetsdBase was one of the first databases with a development environment on PC's. Its latest version dBase V is still sold as dBase classic, which needs a DOS Emulation. The up-to-date product is dBase plus.Scalable in-memory time series database optimized for IoT and Big DataA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwaredata warehouse software for querying and managing large distributed datasets, built on Hadoop
Primary database modelDocument storeRelational DBMSTime Series DBMSRelational DBMSRelational DBMS
Secondary database modelsKey-value store
Relational DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.79
Rank#212  Overall
#36  Document stores
Score10.34
Rank#44  Overall
#28  Relational DBMS
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score61.17
Rank#18  Overall
#12  Relational DBMS
Websitewww.bigchaindb.comwww.dbase.comgriddb.netgithub.com/­heavyai/­heavydb
www.heavy.ai
hive.apache.org
Technical documentationbigchaindb.readthedocs.io/­en/­latestwww.dbase.com/­support/­knowledgebasedocs.griddb.netdocs.heavy.aicwiki.apache.org/­confluence/­display/­Hive/­Home
DeveloperAsthon TateToshiba CorporationHEAVY.AI, Inc.Apache Software Foundation infoinitially developed by Facebook
Initial release20161979201320162012
Current releasedBASE 2019, 20195.1, August 20225.10, January 20223.1.3, April 2022
License infoCommercial or Open SourceOpen Source infoAGPL v3commercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2; enterprise edition availableOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languagePythonC++C++ and CUDAJava
Server operating systemsLinuxDOS infodBase Classic
Windows infodBase Pro
LinuxLinuxAll OS with a Java VM
Data schemeschema-freeyesyesyesyes
Typing infopredefined data types such as float or datenoyesyes infonumerical, string, blob, geometry, boolean, timestampyesyes
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.nonono
Secondary indexesyesyesnoyes
SQL infoSupport of SQLnonoSQL92, SQL-like TQL (Toshiba Query Language)yesSQL-like DML and DDL statements
APIs and other access methodsCLI Client
RESTful HTTP API
none infoThe IDE can access other DBMS or ODBC-sources.JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JDBC
ODBC
Thrift
Vega
JDBC
ODBC
Thrift
Supported programming languagesGo
Haskell
Java
JavaScript
Python
Ruby
dBase proprietary IDEC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
All languages supporting JDBC/ODBC/Thrift
Python
C++
Java
PHP
Python
Server-side scripts infoStored proceduresno infoThe IDE can access stored procedures in other database systems.nonoyes infouser defined functions and integration of map-reduce
Triggersnoyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingnoneShardingSharding infoRound robinSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornoneSource-replica replicationMulti-source replicationselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsnoyes infoquery execution via MapReduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency within container, eventual consistency across containersImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infonot for dBase internal data, but IDE does support transactions when accessing external DBMSACID at container levelnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyes,with MongoDB ord RethinkDByesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes
User concepts infoAccess controlyesAccess rights for users and rolesAccess rights for users can be defined per databasefine grained access rights according to SQL-standardAccess rights for users, groups and roles
More information provided by the system vendor
BigchainDBdBASEGridDBHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Hive
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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More resources
BigchainDBdBASEGridDBHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Hive
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