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DBMS > Microsoft Azure Data Explorer vs. Sphinx vs. Splice Machine vs. Weaviate vs. YDB

System Properties Comparison Microsoft Azure Data Explorer vs. Sphinx vs. Splice Machine vs. Weaviate vs. YDB

Editorial information provided by DB-Engines
NameMicrosoft Azure Data Explorer  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparisonWeaviate  Xexclude from comparisonYDB  Xexclude from comparison
DescriptionFully managed big data interactive analytics platformOpen 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 SparkAn AI-native realtime vector database engine that integrates scalable machine learning models.A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API
Primary database modelRelational DBMS infocolumn orientedSearch engineRelational DBMSVector DBMSDocument store
Relational DBMS
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
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Score0.54
Rank#252  Overall
#115  Relational DBMS
Score1.52
Rank#153  Overall
#5  Vector DBMS
Score0.33
Rank#287  Overall
#43  Document stores
#132  Relational DBMS
Websiteazure.microsoft.com/­services/­data-explorersphinxsearch.comsplicemachine.comgithub.com/­weaviate/­weaviate
weaviate.io
github.com/­ydb-platform/­ydb
ydb.tech
Technical documentationdocs.microsoft.com/­en-us/­azure/­data-explorersphinxsearch.com/­docssplicemachine.com/­how-it-worksweaviate.io/­developers/­weaviateydb.tech/­en/­docs
DeveloperMicrosoftSphinx Technologies Inc.Splice MachineWeaviate B.V.Yandex
Initial release20192001201420192019
Current releasecloud service with continuous releases3.5.1, February 20233.1, March 20211.19, May 2023
License infoCommercial or Open SourcecommercialOpen Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license availableOpen Source infocommercial license available with Weaviate EnterpriseOpen Source infoApache 2.0; 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++JavaGo
Server operating systemshostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Linux
Data schemeFixed schema with schema-less datatypes (dynamic)yesyesyes, maps to GraphQL interfaceFlexible Schema (defined schema, partial schema, schema free)
Typing infopredefined data types such as float or dateyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesnoyesyes infostring, int, float, geo point, date, cross reference, fuzzy referencesyes
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.yesnono
Secondary indexesall fields are automatically indexedyes infofull-text index on all search fieldsyesyes infoall data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexedyes
SQL infoSupport of SQLKusto Query Language (KQL), SQL subsetSQL-like query language (SphinxQL)yesGraphQL is used as query languageSQL-like query language (YQL)
APIs and other access methodsMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Proprietary protocolJDBC
Native Spark Datasource
ODBC
GraphQL query language
RESTful HTTP/JSON API
RESTful HTTP API (DynamoDB compatible)
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
JavaScript / TypeScript
Python
Go
Java
JavaScript (Node.js)
PHP
Python
Server-side scripts infoStored proceduresYes, possible languages: KQL, Python, Rnoyes infoJavanono
Triggersyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynoyesno
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud serviceSharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar PartitioningShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.noneMulti-source replication
Source-replica replication
yesActive-passive shard replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsSpark connector (open source): github.com/­Azure/­azure-kusto-sparknoYes, via Full Spark Integrationnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)yesyes
Durability infoSupport for making data persistentyesyes infoThe original contents of fields are not stored in the Sphinx index.yesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesno
User concepts infoAccess controlAzure Active Directory AuthenticationnoAccess rights for users, groups and roles according to SQL-standardAPI Keys
OpenID Connect Discovery
Access rights defined for Yandex Cloud users
More information provided by the system vendor
Microsoft Azure Data ExplorerSphinxSplice MachineWeaviateYDB
Specific characteristicsWeaviate is an open source vector database that is robust, scalable, cloud-native,...
» more
Competitive advantagesFlexible deployment - Free, open source or fully-managed cloud vector database service...
» more
Typical application scenariosAs a database supporting the development of generative AI and semantic search applications...
» more
Key customersAll companies that have data. ​
» more
Market metricsAs of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
» more
Licensing and pricing modelsWeaviate is open-source, and free to use. Weaviate is also available as a fully managed...
» more

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More resources
Microsoft Azure Data ExplorerSphinxSplice MachineWeaviateYDB
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