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DBMS > HBase vs. Microsoft Azure Data Explorer vs. SWC-DB

System Properties Comparison HBase vs. Microsoft Azure Data Explorer vs. SWC-DB

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Editorial information provided by DB-Engines
NameHBase  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSWC-DB infoSuper Wide Column Database  Xexclude from comparison
DescriptionWide-column store based on Apache Hadoop and on concepts of BigTableFully managed big data interactive analytics platformA high performance, scalable Wide Column DBMS
Primary database modelWide column storeRelational DBMS infocolumn orientedWide column store
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
Score30.50
Rank#26  Overall
#2  Wide column stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.01
Rank#376  Overall
#13  Wide column stores
Websitehbase.apache.orgazure.microsoft.com/­services/­data-explorergithub.com/­kashirin-alex/­swc-db
www.swcdb.org
Technical documentationhbase.apache.org/­book.htmldocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software Foundation infoApache top-level project, originally developed by PowersetMicrosoftAlex Kashirin
Initial release200820192020
Current release2.3.4, January 2021cloud service with continuous releases0.5, April 2021
License infoCommercial or Open SourceOpen Source infoApache version 2commercialOpen Source infoGPL V3
Cloud-based only infoOnly available as a cloud servicenoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++
Server operating systemsLinux
Unix
Windows infousing Cygwin
hostedLinux
Data schemeschema-free, schema definition possibleFixed schema with schema-less datatypes (dynamic)schema-free
Typing infopredefined data types such as float or dateoptions to bring your own types, AVROyes 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.noyesno
Secondary indexesnoall fields are automatically indexed
SQL infoSupport of SQLnoKusto Query Language (KQL), SQL subsetSQL-like query language
APIs and other access methodsJava API
RESTful HTTP API
Thrift
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Proprietary protocol
Thrift
Supported programming languagesC
C#
C++
Groovy
Java
PHP
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C++
Server-side scripts infoStored proceduresyes infoCoprocessors in JavaYes, possible languages: KQL, Python, Rno
Triggersyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataSingle row ACID (across millions of columns)no
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnono
User concepts infoAccess controlAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACAzure Active Directory Authentication

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More resources
HBaseMicrosoft Azure Data ExplorerSWC-DB infoSuper Wide Column Database
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