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DBMS > Cubrid vs. HEAVY.AI vs. Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. OrientDB

System Properties Comparison Cubrid vs. HEAVY.AI vs. Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. OrientDB

Editorial information provided by DB-Engines
NameCubrid  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonOrientDB  Xexclude from comparison
DescriptionCUBRID is an open-source SQL-based relational database management system with object extensions for OLTPA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareFully managed big data interactive analytics platformWidely used in-process key-value storeMulti-model DBMS (Document, Graph, Key/Value)
Primary database modelRelational DBMSRelational DBMSRelational DBMS infocolumn orientedKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Document store
Graph DBMS
Key-value store
Secondary database modelsSpatial DBMSDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#169  Overall
#78  Relational DBMS
Score2.10
Rank#126  Overall
#61  Relational DBMS
Score5.16
Rank#69  Overall
#37  Relational DBMS
Score2.52
Rank#114  Overall
#20  Key-value stores
#3  Native XML DBMS
Score3.27
Rank#97  Overall
#16  Document stores
#6  Graph DBMS
#15  Key-value stores
Websitecubrid.com (korean)
cubrid.org (english)
github.com/­heavyai/­heavydb
www.heavy.ai
azure.microsoft.com/­services/­data-explorerwww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlorientdb.org
Technical documentationcubrid.org/­manualsdocs.heavy.aidocs.microsoft.com/­en-us/­azure/­data-explorerdocs.oracle.com/­cd/­E17076_05/­html/­index.htmlwww.orientdb.com/­docs/­last/­index.html
DeveloperCUBRID Corporation, CUBRID FoundationHEAVY.AI, Inc.MicrosoftOracle infooriginally developed by Sleepycat, which was acquired by OracleOrientDB LTD; CallidusCloud; SAP
Initial release20082016201919942010
Current release11.0, January 20215.10, January 2022cloud service with continuous releases18.1.40, May 20203.2.29, March 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2; enterprise edition availablecommercialOpen Source infocommercial license availableOpen Source infoApache version 2
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++, JavaC++ and CUDAC, Java, C++ (depending on the Berkeley DB edition)Java
Server operating systemsLinux
Windows
LinuxhostedAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
All OS with a Java JDK (>= JDK 6)
Data schemeyesyesFixed schema with schema-less datatypes (dynamic)schema-freeschema-free infoSchema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesnoyes
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.nonoyesyes infoonly with the Berkeley DB XML editionno
Secondary indexesyesnoall fields are automatically indexedyesyes
SQL infoSupport of SQLyesyesKusto Query Language (KQL), SQL subsetyes infoSQL interfaced based on SQLite is availableSQL-like query language, no joins
APIs and other access methodsADO.NET
JDBC
ODBC
OLE DB
JDBC
ODBC
Thrift
Vega
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API
Supported programming languagesC
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
All languages supporting JDBC/ODBC/Thrift
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresJava Stored ProceduresnoYes, possible languages: KQL, Python, RnoJava, Javascript
Triggersyesnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoonly for the SQL APIHooks
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infoRound robinSharding infoImplicit feature of the cloud servicenoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-source replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationMulti-source replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono infocould be achieved with distributed queries
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesnononoyes inforelationship in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnoyes
User concepts infoAccess controlfine grained access rights according to SQL-standardfine grained access rights according to SQL-standardAzure Active Directory AuthenticationnoAccess rights for users and roles; record level security configurable

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More resources
CubridHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Microsoft Azure Data ExplorerOracle Berkeley DBOrientDB
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