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DBMS > Apache Druid vs. GeoMesa vs. Microsoft Azure AI Search vs. Netezza

System Properties Comparison Apache Druid vs. GeoMesa vs. Microsoft Azure AI Search vs. Netezza

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGeoMesa  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataGeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.Search-as-a-service for web and mobile app developmentData warehouse and analytics appliance part of IBM PureSystems
Primary database modelRelational DBMS
Time Series DBMS
Spatial DBMSSearch engineRelational DBMS
Secondary database modelsVector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.25
Rank#90  Overall
#47  Relational DBMS
#7  Time Series DBMS
Score0.86
Rank#205  Overall
#4  Spatial DBMS
Score5.52
Rank#59  Overall
#6  Search engines
Score8.59
Rank#45  Overall
#29  Relational DBMS
Websitedruid.apache.orgwww.geomesa.orgazure.microsoft.com/­en-us/­services/­searchwww.ibm.com/­products/­netezza
Technical documentationdruid.apache.org/­docs/­latest/­designwww.geomesa.org/­documentation/­stable/­user/­index.htmllearn.microsoft.com/­en-us/­azure/­search
DeveloperApache Software Foundation and contributorsCCRi and othersMicrosoftIBM
Initial release2012201420152000
Current release29.0.1, April 20245.0.0, May 2024V1
License infoCommercial or Open SourceOpen Source infoApache license v2Open Source infoApache License 2.0commercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScala
Server operating systemsLinux
OS X
Unix
hostedLinux infoincluded in appliance
Data schemeyes infoschema-less columns are supportedyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.nonono
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL for queryingnonoyes
APIs and other access methodsJDBC
RESTful HTTP/JSON API
RESTful HTTP APIJDBC
ODBC
OLE DB
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C#
Java
JavaScript
Python
C
C++
Fortran
Java
Lua
Perl
Python
R
Server-side scripts infoStored proceduresnononoyes
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-baseddepending on storage layerSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesdepending on storage layeryes infoImplicit feature of the cloud serviceSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesnoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistencydepending on storage layerImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononoACID
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.nodepending on storage layerno
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemyes infodepending on the DBMS used for storageyes infousing Azure authenticationUsers with fine-grained authorization concept

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More resources
Apache DruidGeoMesaMicrosoft Azure AI SearchNetezza infoAlso called PureData System for Analytics by IBM
DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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