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DBMS > Apache IoTDB vs. Microsoft Azure Data Explorer vs. OpenTSDB vs. Oracle Berkeley DB vs. SAP SQL Anywhere

System Properties Comparison Apache IoTDB vs. Microsoft Azure Data Explorer vs. OpenTSDB vs. Oracle Berkeley DB vs. SAP SQL Anywhere

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonOpenTSDB  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonSAP SQL Anywhere infoformerly called Adaptive Server Anywhere  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 platformScalable Time Series DBMS based on HBaseWidely used in-process key-value storeRDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments
Primary database modelTime Series DBMSRelational DBMS infocolumn orientedTime Series DBMSKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Relational 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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score1.68
Rank#146  Overall
#12  Time Series DBMS
Score2.21
Rank#117  Overall
#20  Key-value stores
#3  Native XML DBMS
Score4.25
Rank#79  Overall
#43  Relational DBMS
Websiteiotdb.apache.orgazure.microsoft.com/­services/­data-exploreropentsdb.netwww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlwww.sap.com/­products/­technology-platform/­sql-anywhere.html
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.microsoft.com/­en-us/­azure/­data-exploreropentsdb.net/­docs/­build/­html/­index.htmldocs.oracle.com/­cd/­E17076_05/­html/­index.htmlhelp.sap.com/­docs/­SAP_SQL_Anywhere
DeveloperApache Software FoundationMicrosoftcurrently maintained by Yahoo and other contributorsOracle infooriginally developed by Sleepycat, which was acquired by OracleSAP infoformerly Sybase
Initial release20182019201119941992
Current release1.1.0, April 2023cloud service with continuous releases18.1.40, May 202017, July 2015
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoLGPLOpen Source infocommercial license availablecommercial
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaC, Java, C++ (depending on the Berkeley DB edition)
Server operating systemsAll OS with a Java VM (>= 1.8)hostedLinux
Windows
AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
AIX
HP-UX
Linux
OS X
Solaris
Windows
Data schemeyesFixed schema with schema-less datatypes (dynamic)schema-freeschema-freeyes
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-typesnumeric data for metrics, strings for tagsnoyes
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.noyesnoyes infoonly with the Berkeley DB XML editionyes
Secondary indexesyesall fields are automatically indexednoyesyes
SQL infoSupport of SQLSQL-like query languageKusto Query Language (KQL), SQL subsetnoyes infoSQL interfaced based on SQLite is availableyes
APIs and other access methodsJDBC
Native API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
HTTP API
Telnet API
ADO.NET
HTTP API
JDBC
ODBC
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Erlang
Go
Java
Python
R
Ruby
.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
C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Server-side scripts infoStored proceduresyesYes, possible languages: KQL, Python, Rnonoyes, in C/C++, Java, .Net or Perl
Triggersyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynoyes infoonly for the SQL APIyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Sharding infoImplicit feature of the cloud serviceSharding infobased on HBasenonenone
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.selectable replication factor infobased on HBaseSource-replica replicationSource-replica replication infoDatabase mirroring
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 Consistency infobased on HBaseImmediate Consistency
Foreign keys infoReferential integritynonononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononoACIDACID
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.yesnonoyesyes
User concepts infoAccess controlyesAzure Active Directory Authenticationnonofine grained access rights according to SQL-standard

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More resources
Apache IoTDBMicrosoft Azure Data ExplorerOpenTSDBOracle Berkeley DBSAP SQL Anywhere infoformerly called Adaptive Server Anywhere
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