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DBMS > FeatureBase vs. Linter vs. Microsoft Azure Data Explorer

System Properties Comparison FeatureBase vs. Linter vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines
NameFeatureBase  Xexclude from comparisonLinter  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionReal-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.RDBMS for high security requirementsFully managed big data interactive analytics platform
Primary database modelRelational DBMSRelational DBMSRelational DBMS infocolumn oriented
Secondary database modelsSpatial DBMSDocument 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
Score0.26
Rank#304  Overall
#138  Relational DBMS
Score0.10
Rank#350  Overall
#153  Relational DBMS
Score5.16
Rank#69  Overall
#37  Relational DBMS
Websitewww.featurebase.comlinter.ruazure.microsoft.com/­services/­data-explorer
Technical documentationdocs.featurebase.comdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperMolecula and Pilosa Open Source Contributorsrelex.ruMicrosoft
Initial release201719902019
Current release2022, May 2022cloud service with continuous releases
License infoCommercial or Open Sourcecommercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoC and C++
Server operating systemsLinux
macOS
AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows
hosted
Data schemeyesyesFixed schema with schema-less datatypes (dynamic)
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-types
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.nonoyes
Secondary indexesnoyesall fields are automatically indexed
SQL infoSupport of SQLSQL queriesyesKusto Query Language (KQL), SQL subset
APIs and other access methodsgRPC
JDBC
Kafka Connector
ODBC
ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesJava
Python
C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresyes infoproprietary syntax with the possibility to convert from PL/SQLYes, possible languages: KQL, Python, R
Triggersnoyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesShardingnoneSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesyesSource-replica replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyes, using Linux fsyncyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesno
User concepts infoAccess controlfine grained access rights according to SQL-standardAzure Active Directory Authentication

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More resources
FeatureBaseLinterMicrosoft Azure Data Explorer
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