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DBMS > HEAVY.AI vs. Microsoft Access vs. Microsoft Azure Data Explorer vs. SiriDB

System Properties Comparison HEAVY.AI vs. Microsoft Access vs. Microsoft Azure Data Explorer vs. SiriDB

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Editorial information provided by DB-Engines
NameHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSiriDB  Xexclude from comparison
DescriptionA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareMicrosoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries. infoThe Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)Fully managed big data interactive analytics platformOpen Source Time Series DBMS
Primary database modelRelational DBMSRelational DBMSRelational DBMS infocolumn orientedTime Series DBMS
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
Score1.41
Rank#153  Overall
#71  Relational DBMS
Score93.76
Rank#12  Overall
#8  Relational DBMS
Score3.28
Rank#83  Overall
#45  Relational DBMS
Score0.00
Rank#385  Overall
#40  Time Series DBMS
Websitegithub.com/­heavyai/­heavydb
www.heavy.ai
www.microsoft.com/­en-us/­microsoft-365/­accessazure.microsoft.com/­services/­data-explorersiridb.com
Technical documentationdocs.heavy.aideveloper.microsoft.com/­en-us/­accessdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.siridb.com
DeveloperHEAVY.AI, Inc.MicrosoftMicrosoftCesbit
Initial release2016199220192017
Current release5.10, January 20221902 (16.0.11328.20222), March 2019cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2; enterprise edition availablecommercial infoBundled with Microsoft OfficecommercialOpen Source infoMIT License
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++ and CUDAC++C
Server operating systemsLinuxWindows infoNot a real database server, but making use of DLLshostedLinux
Data schemeyesyesFixed schema with schema-less datatypes (dynamic)yes
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-typesyes infoNumeric data
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.noyesno
Secondary indexesnoyesall fields are automatically indexedyes
SQL infoSupport of SQLyesyes infobut not compliant to any SQL standardKusto Query Language (KQL), SQL subsetno
APIs and other access methodsJDBC
ODBC
Thrift
Vega
ADO.NET
DAO
ODBC
OLE DB
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
HTTP API
Supported programming languagesAll languages supporting JDBC/ODBC/Thrift
Python
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
Server-side scripts infoStored proceduresnoyes infosince Access 2010 using the ACE-engineYes, possible languages: KQL, Python, Rno
Triggersnoyes infosince Access 2010 using the ACE-engineyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesSharding infoRound robinnoneSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationnoneyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infobut no files for transaction loggingnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infobut no files for transaction loggingyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes
User concepts infoAccess controlfine grained access rights according to SQL-standardno infoa simple user-level security was built in till version Access 2003Azure Active Directory Authenticationsimple rights management via user accounts

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More resources
HEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Microsoft AccessMicrosoft Azure Data ExplorerSiriDB
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