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DBMS > Microsoft Azure Data Explorer vs. ObjectBox vs. Spark SQL vs. Yanza

System Properties Comparison Microsoft Azure Data Explorer vs. ObjectBox vs. Spark SQL vs. Yanza

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Editorial information provided by DB-Engines
NameMicrosoft Azure Data Explorer  Xexclude from comparisonObjectBox  Xexclude from comparisonSpark SQL  Xexclude from comparisonYanza  Xexclude from comparison
Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionFully managed big data interactive analytics platformExtremely fast embedded database for small devices, IoT and MobileSpark SQL is a component on top of 'Spark Core' for structured data processingTime Series DBMS for IoT Applications
Primary database modelRelational DBMS infocolumn orientedObject oriented DBMSRelational DBMSTime Series 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
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score1.20
Rank#170  Overall
#5  Object oriented DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websiteazure.microsoft.com/­services/­data-explorerobjectbox.iospark.apache.org/­sqlyanza.com
Technical documentationdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.objectbox.iospark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperMicrosoftObjectBox LimitedApache Software FoundationYanza
Initial release2019201720142015
Current releasecloud service with continuous releases3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache License 2.0Open Source infoApache 2.0commercial infofree version available
Cloud-based only infoOnly available as a cloud serviceyesnonono infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC and C++Scala
Server operating systemshostedAndroid
iOS
Linux
macOS
Windows
Linux
OS X
Windows
Windows
Data schemeFixed schema with schema-less datatypes (dynamic)yesyesschema-free
Typing infopredefined data types such as float or dateyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesyesno
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.yesnonono
Secondary indexesall fields are automatically indexedyesnono
SQL infoSupport of SQLKusto Query Language (KQL), SQL subsetnoSQL-like DML and DDL statementsno
APIs and other access methodsMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Proprietary native APIJDBC
ODBC
HTTP API
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C++
Dart
Go
Java
JavaScript infoplanned (as of Jan 2019)
Kotlin
Python infoplanned (as of Jan 2019)
Swift
Java
Python
R
Scala
any language that supports HTTP calls
Server-side scripts infoStored proceduresYes, possible languages: KQL, Python, Rnonono
Triggersyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynonoyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud servicenoneyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.online/offline synchronization between client and servernonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlAzure Active Directory Authenticationyesnono
More information provided by the system vendor
Microsoft Azure Data ExplorerObjectBoxSpark SQLYanza
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More resources
Microsoft Azure Data ExplorerObjectBoxSpark SQLYanza
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