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DBMS > Apache IoTDB vs. Microsoft Azure Data Explorer vs. Microsoft Azure SQL Database vs. Microsoft SQL Server vs. Sadas Engine

System Properties Comparison Apache IoTDB vs. Microsoft Azure Data Explorer vs. Microsoft Azure SQL Database vs. Microsoft SQL Server vs. Sadas Engine

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonMicrosoft Azure SQL Database infoformerly SQL Azure  Xexclude from comparisonMicrosoft SQL Server  Xexclude from comparisonSadas Engine  Xexclude from comparison
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkFully managed big data interactive analytics platformDatabase as a Service offering with high compatibility to Microsoft SQL ServerMicrosofts flagship relational DBMSSADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments
Primary database modelTime Series DBMSRelational DBMS infocolumn orientedRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument 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
Document store
Graph DBMS
Spatial DBMS
Document store
Graph DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score76.78
Rank#16  Overall
#11  Relational DBMS
Score821.56
Rank#3  Overall
#3  Relational DBMS
Score0.07
Rank#373  Overall
#157  Relational DBMS
Websiteiotdb.apache.orgazure.microsoft.com/­services/­data-explorerazure.microsoft.com/­en-us/­products/­azure-sql/­databasewww.microsoft.com/­en-us/­sql-serverwww.sadasengine.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.microsoft.com/­en-us/­azure/­data-explorerdocs.microsoft.com/­en-us/­azure/­azure-sqllearn.microsoft.com/­en-US/­sql/­sql-serverwww.sadasengine.com/­en/­sadas-engine-download-free-trial-and-documentation/­#documentation
DeveloperApache Software FoundationMicrosoftMicrosoftMicrosoftSADAS s.r.l.
Initial release20182019201019892006
Current release1.1.0, April 2023cloud service with continuous releasesV12SQL Server 2022, November 20228.0
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialcommercial inforestricted free version is availablecommercial infofree trial version available
Cloud-based only infoOnly available as a cloud servicenoyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
SQLServer Flex @ STACKIT offers a managed version of SQL Server with adjustable CPU, RAM, storage amount and speed, in enterprise grade to perfectly match all application requirements. All services are 100% GDPR-compliant.
Implementation languageJavaC++C++C++
Server operating systemsAll OS with a Java VM (>= 1.8)hostedhostedLinux
Windows
AIX
Linux
Windows
Data schemeyesFixed schema with schema-less datatypes (dynamic)yesyesyes
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesyesyes
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.noyesyesyesno
Secondary indexesyesall fields are automatically indexedyesyesyes
SQL infoSupport of SQLSQL-like query languageKusto Query Language (KQL), SQL subsetyesyesyes
APIs and other access methodsJDBC
Native API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
ODBC
ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)
JDBC
ODBC
Proprietary protocol
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic
.Net
C
C#
C++
Groovy
Java
PHP
Python
Server-side scripts infoStored proceduresyesYes, possible languages: KQL, Python, RTransact SQLTransact SQL, .NET languages, R, Python and (with SQL Server 2019) Javano
Triggersyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesyesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Sharding infoImplicit feature of the cloud servicetables can be distributed across several files (horizontal partitioning); sharding through federationhorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes, with always 3 replicas availableyes, but depending on the SQL-Server Editionnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparkSpark connector (open source): github.com/­Azure/­azure-kusto-sparknonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Eventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
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.yesnoyesyes infomanaged by 'Learn by Usage'
User concepts infoAccess controlyesAzure Active Directory Authenticationfine grained access rights according to SQL-standardfine grained access rights according to SQL-standardAccess rights for users, groups and roles according to SQL-standard

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More resources
Apache IoTDBMicrosoft Azure Data ExplorerMicrosoft Azure SQL Database infoformerly SQL AzureMicrosoft SQL ServerSadas Engine
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