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DBMS > Apache Phoenix vs. Kinetica vs. Microsoft Azure Data Explorer vs. Quasardb

System Properties Comparison Apache Phoenix vs. Kinetica vs. Microsoft Azure Data Explorer vs. Quasardb

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonKinetica  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonQuasardb  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseFully vectorized database across both GPUs and CPUsFully managed big data interactive analytics platformDistributed, high-performance timeseries database
Primary database modelRelational DBMSRelational DBMSRelational DBMS infocolumn orientedTime Series DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Document 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
Score1.90
Rank#125  Overall
#59  Relational DBMS
Score0.42
Rank#261  Overall
#120  Relational DBMS
Score3.28
Rank#83  Overall
#45  Relational DBMS
Score0.13
Rank#332  Overall
#28  Time Series DBMS
Websitephoenix.apache.orgwww.kinetica.comazure.microsoft.com/­services/­data-explorerquasar.ai
Technical documentationphoenix.apache.orgdocs.kinetica.comdocs.microsoft.com/­en-us/­azure/­data-explorerdoc.quasar.ai/­master
DeveloperApache Software FoundationKineticaMicrosoftquasardb
Initial release2014201220192009
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20197.1, August 2021cloud service with continuous releases3.14.1, January 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialcommercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licenses
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC, C++C++
Server operating systemsLinux
Unix
Windows
LinuxhostedBSD
Linux
OS X
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesyesFixed schema with schema-less datatypes (dynamic)schema-free
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-typesyes infointeger and binary
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.nonoyesno
Secondary indexesyesyesall fields are automatically indexedyes infowith tags
SQL infoSupport of SQLyesSQL-like DML and DDL statementsKusto Query Language (KQL), SQL subsetSQL-like query language
APIs and other access methodsJDBCJDBC
ODBC
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
HTTP API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C++
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
Server-side scripts infoStored proceduresuser defined functionsuser defined functionsYes, possible languages: KQL, Python, Rno
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangeyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud serviceSharding infoconsistent hashing
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Source-replica replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replication with selectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkwith Hadoop integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoby using LevelDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infoGPU vRAM or System RAMnoyes infoTransient mode
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users and roles on table levelAzure Active Directory AuthenticationCryptographically strong user authentication and audit trail

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More resources
Apache PhoenixKineticaMicrosoft Azure Data ExplorerQuasardb
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