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DBMS > Apache IoTDB vs. EsgynDB vs. Microsoft Azure Data Explorer vs. Riak KV vs. SAP SQL Anywhere

System Properties Comparison Apache IoTDB vs. EsgynDB vs. Microsoft Azure Data Explorer vs. Riak KV vs. SAP SQL Anywhere

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonEsgynDB  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonRiak KV  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 FlinkEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionFully managed big data interactive analytics platformDistributed, fault tolerant key-value storeRDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments
Primary database modelTime Series DBMSRelational DBMSRelational DBMS infocolumn orientedKey-value store infowith links between data sets and object tags for the creation of secondary indexesRelational 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
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score0.25
Rank#312  Overall
#138  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score4.01
Rank#79  Overall
#9  Key-value stores
Score3.95
Rank#80  Overall
#42  Relational DBMS
Websiteiotdb.apache.orgwww.esgyn.cnazure.microsoft.com/­services/­data-explorerwww.sap.com/­products/­technology-platform/­sql-anywhere.html
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.microsoft.com/­en-us/­azure/­data-explorerwww.tiot.jp/­riak-docs/­riak/­kv/­latesthelp.sap.com/­docs/­SAP_SQL_Anywhere
DeveloperApache Software FoundationEsgynMicrosoftOpenSource, formerly Basho TechnologiesSAP infoformerly Sybase
Initial release20182015201920091992
Current release1.1.0, April 2023cloud service with continuous releases3.2.0, December 202217, July 2015
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infoApache version 2, commercial enterprise editioncommercial
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 languageJavaC++, JavaErlang
Server operating systemsAll OS with a Java VM (>= 1.8)LinuxhostedLinux
OS X
AIX
HP-UX
Linux
OS X
Solaris
Windows
Data schemeyesyesFixed schema with schema-less datatypes (dynamic)schema-freeyes
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.nonoyesnoyes
Secondary indexesyesyesall fields are automatically indexedrestrictedyes
SQL infoSupport of SQLSQL-like query languageyesKusto Query Language (KQL), SQL subsetnoyes
APIs and other access methodsJDBC
Native API
ADO.NET
JDBC
ODBC
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
HTTP API
Native Erlang Interface
ADO.NET
HTTP API
JDBC
ODBC
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
All languages supporting JDBC/ODBC/ADO.Net.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Server-side scripts infoStored proceduresyesJava Stored ProceduresYes, possible languages: KQL, Python, RErlangyes, in C/C++, Java, .Net or Perl
Triggersyesnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infopre-commit hooks and post-commit hooksyes
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 infono "single point of failure"none
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication between multi datacentersyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.selectable replication factorSource-replica replication infoDatabase mirroring
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparkyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparkyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyEventual Consistency
Immediate Consistency
Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnono infolinks between data sets can be storedyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnonoACID
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.yesnonoyes
User concepts infoAccess controlyesfine grained access rights according to SQL-standardAzure Active Directory Authenticationyes, using Riak Securityfine grained access rights according to SQL-standard

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More resources
Apache IoTDBEsgynDBMicrosoft Azure Data ExplorerRiak KVSAP SQL Anywhere infoformerly called Adaptive Server Anywhere
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