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

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

Editorial information provided by DB-Engines
NameMicrosoft Azure Data Explorer  Xexclude from comparisonOracle NoSQL  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparisonYDB  Xexclude from comparison
DescriptionFully managed big data interactive analytics platformA multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodesOpen 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 SparkA 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 orientedDocument store
Key-value store
Relational DBMS
Search engineRelational 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
Score3.05
Rank#97  Overall
#17  Document stores
#16  Key-value stores
#50  Relational DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Score0.54
Rank#252  Overall
#115  Relational DBMS
Score0.33
Rank#287  Overall
#43  Document stores
#132  Relational DBMS
Websiteazure.microsoft.com/­services/­data-explorerwww.oracle.com/­database/­nosql/­technologies/­nosqlsphinxsearch.comsplicemachine.comgithub.com/­ydb-platform/­ydb
ydb.tech
Technical documentationdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.oracle.com/­en/­database/­other-databases/­nosql-database/­index.htmlsphinxsearch.com/­docssplicemachine.com/­how-it-worksydb.tech/­en/­docs
DeveloperMicrosoftOracleSphinx Technologies Inc.Splice MachineYandex
Initial release20192011200120142019
Current releasecloud service with continuous releases24.1, May 20243.5.1, February 20233.1, March 2021
License infoCommercial or Open SourcecommercialOpen Source infoProprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)Open Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license availableOpen 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 languageJavaC++Java
Server operating systemshostedLinux
Solaris SPARC/x86
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Linux
Data schemeFixed schema with schema-less datatypes (dynamic)Support Fixed schema and Schema-less deployment with the ability to interoperate between them.yesyesFlexible 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-typesoptionalnoyesyes
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 indexedyesyes infofull-text index on all search fieldsyesyes
SQL infoSupport of SQLKusto Query Language (KQL), SQL subsetSQL-like DML and DDL statementsSQL-like query language (SphinxQL)yesSQL-like query language (YQL)
APIs and other access methodsMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
RESTful HTTP APIProprietary protocolJDBC
Native Spark Datasource
ODBC
RESTful HTTP API (DynamoDB compatible)
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C#
Go
Java
JavaScript (Node.js)
Python
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Go
Java
JavaScript (Node.js)
PHP
Python
Server-side scripts infoStored proceduresYes, possible languages: KQL, Python, Rnonoyes infoJavano
Triggersyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynonoyes
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud serviceShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar PartitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Electable source-replica replication per shard. Support distributed global deployment with Multi-region table featurenoneMulti-source replication
Source-replica replication
Active-passive shard replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsSpark connector (open source): github.com/­Azure/­azure-kusto-sparkwith Hadoop integrationnoYes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency infodepending on configuration
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoconfigurable infoACID within a storage node (=shard)noACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infooff heap cacheyesno
User concepts infoAccess controlAzure Active Directory AuthenticationAccess rights for users and rolesnoAccess rights for users, groups and roles according to SQL-standardAccess rights defined for Yandex Cloud users

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More resources
Microsoft Azure Data ExplorerOracle NoSQLSphinxSplice MachineYDB
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