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DBMS > atoti vs. HBase vs. Microsoft Azure Data Explorer

System Properties Comparison atoti vs. HBase vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines
Nameatoti  Xexclude from comparisonHBase  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionAn in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.Wide-column store based on Apache Hadoop and on concepts of BigTableFully managed big data interactive analytics platform
Primary database modelObject oriented DBMSWide column storeRelational DBMS infocolumn oriented
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
Score0.56
Rank#245  Overall
#10  Object oriented DBMS
Score30.50
Rank#26  Overall
#2  Wide column stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websiteatoti.iohbase.apache.orgazure.microsoft.com/­services/­data-explorer
Technical documentationdocs.atoti.iohbase.apache.org/­book.htmldocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperActiveViamApache Software Foundation infoApache top-level project, originally developed by PowersetMicrosoft
Initial release20082019
Current release2.3.4, January 2021cloud service with continuous releases
License infoCommercial or Open Sourcecommercial infofree versions availableOpen Source infoApache version 2commercial
Cloud-based only infoOnly available as a cloud servicenonoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJava
Server operating systemsLinux
Unix
Windows infousing Cygwin
hosted
Data schemeschema-free, schema definition possibleFixed schema with schema-less datatypes (dynamic)
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.noyes
Secondary indexesnoall fields are automatically indexed
SQL infoSupport of SQLMultidimensional Expressions (MDX)noKusto Query Language (KQL), SQL subset
APIs and other access methodsJava API
RESTful HTTP API
Thrift
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesC
C#
C++
Groovy
Java
PHP
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresPythonyes infoCoprocessors in JavaYes, possible languages: KQL, Python, R
Triggersyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesSharding, horizontal partitioningShardingSharding infoImplicit feature of the cloud service
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 methodsnoyesSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual 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 datayes, multi-version concurrency control (MVCC)yesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
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
atotiHBaseMicrosoft Azure Data Explorer
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