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DBMS > Microsoft Azure AI Search vs. Netezza vs. ObjectBox vs. Sphinx vs. Splice Machine

System Properties Comparison Microsoft Azure AI Search vs. Netezza vs. ObjectBox vs. Sphinx vs. Splice Machine

Editorial information provided by DB-Engines
NameMicrosoft Azure AI Search  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonObjectBox  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionSearch-as-a-service for web and mobile app developmentData warehouse and analytics appliance part of IBM PureSystemsExtremely fast embedded database for small devices, IoT and MobileOpen source search engine for searching in data from different sources, e.g. relational databasesOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelSearch engineRelational DBMSObject oriented DBMSSearch engineRelational DBMS
Secondary database modelsVector DBMSTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score5.72
Rank#66  Overall
#8  Search engines
Score11.20
Rank#46  Overall
#29  Relational DBMS
Score1.42
Rank#169  Overall
#5  Object oriented DBMS
Score6.06
Rank#61  Overall
#6  Search engines
Score0.54
Rank#257  Overall
#118  Relational DBMS
Websiteazure.microsoft.com/­en-us/­services/­searchwww.ibm.com/­products/­netezzaobjectbox.iosphinxsearch.comsplicemachine.com
Technical documentationlearn.microsoft.com/­en-us/­azure/­searchdocs.objectbox.iosphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperMicrosoftIBMObjectBox LimitedSphinx Technologies Inc.Splice Machine
Initial release20152000201720012014
Current releaseV13.5.1, February 20233.1, March 2021
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache License 2.0Open Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC and C++C++Java
Server operating systemshostedLinux infoincluded in applianceAndroid
iOS
Linux
macOS
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeyesyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesnoyes
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.nono
Secondary indexesyesyesyesyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLnoyesnoSQL-like query language (SphinxQL)yes
APIs and other access methodsRESTful HTTP APIJDBC
ODBC
OLE DB
Proprietary native APIProprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languagesC#
Java
JavaScript
Python
C
C++
Fortran
Java
Lua
Perl
Python
R
C
C++
Dart
Go
Java
JavaScript infoplanned (as of Jan 2019)
Kotlin
Python infoplanned (as of Jan 2019)
Swift
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnoyesnonoyes infoJava
Triggersnonononoyes
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud serviceShardingnoneSharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoImplicit feature of the cloud serviceSource-replica replicationonline/offline synchronization between client and servernoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesnonoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlyes infousing Azure authenticationUsers with fine-grained authorization conceptyesnoAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
Microsoft Azure AI SearchNetezza infoAlso called PureData System for Analytics by IBMObjectBoxSphinxSplice Machine
News

In-Memory Database Use Cases
15 February 2024

Data Viewer for Objects – announcing ObjectBox Admin
14 November 2023

Vector Databases for Edge AI
9 August 2023

Vector Database Release for Flutter / Dart + Python
13 June 2023

SQLite and SQLite alternatives – a comprehensive overview
7 June 2023

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