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DBMS > Apache IoTDB vs. Hazelcast vs. Microsoft Azure Data Explorer vs. Microsoft Azure SQL Database vs. Trafodion

System Properties Comparison Apache IoTDB vs. Hazelcast vs. Microsoft Azure Data Explorer vs. Microsoft Azure SQL Database vs. Trafodion

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonHazelcast  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonMicrosoft Azure SQL Database infoformerly SQL Azure  Xexclude from comparisonTrafodion  Xexclude from comparison
Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.
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 FlinkA widely adopted in-memory data gridFully managed big data interactive analytics platformDatabase as a Service offering with high compatibility to Microsoft SQL ServerTransactional SQL-on-Hadoop DBMS
Primary database modelTime Series DBMSKey-value storeRelational DBMS infocolumn orientedRelational DBMSRelational DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12Document 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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score77.99
Rank#16  Overall
#11  Relational DBMS
Websiteiotdb.apache.orghazelcast.comazure.microsoft.com/­services/­data-explorerazure.microsoft.com/­en-us/­products/­azure-sql/­databasetrafodion.apache.org
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlhazelcast.org/­imdg/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.microsoft.com/­en-us/­azure/­azure-sqltrafodion.apache.org/­documentation.html
DeveloperApache Software FoundationHazelcastMicrosoftMicrosoftApache Software Foundation, originally developed by HP
Initial release20182008201920102014
Current release1.1.0, April 20235.3.6, November 2023cloud service with continuous releasesV122.3.0, February 2019
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2; commercial licenses availablecommercialcommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaC++C++, Java
Server operating systemsAll OS with a Java VM (>= 1.8)All OS with a Java VMhostedhostedLinux
Data schemeyesschema-freeFixed schema with schema-less datatypes (dynamic)yesyes
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-typesyesyes
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.noyes infothe object must implement a serialization strategyyesyesno
Secondary indexesyesyesall fields are automatically indexedyesyes
SQL infoSupport of SQLSQL-like query languageSQL-like query languageKusto Query Language (KQL), SQL subsetyesyes
APIs and other access methodsJDBC
Native API
JCache
JPA
Memcached protocol
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
ODBC
ADO.NET
JDBC
ODBC
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby
All languages supporting JDBC/ODBC/ADO.Net
Server-side scripts infoStored proceduresyesyes infoEvent Listeners, Executor ServicesYes, possible languages: KQL, Python, RTransact SQLJava Stored Procedures
Triggersyesyes infoEventsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingSharding infoImplicit feature of the cloud serviceSharding
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 infoReplicated Mapyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes, with always 3 replicas availableyes, via HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparkyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparknoyes infovia user defined functions and HBase
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoone or two-phase-commit; repeatable reads; read commitednoACIDACID
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.yesyesnono
User concepts infoAccess controlyesRole-based access controlAzure Active Directory Authenticationfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
Apache IoTDBHazelcastMicrosoft Azure Data ExplorerMicrosoft Azure SQL Database infoformerly SQL AzureTrafodion
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