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DBMS > Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. Sphinx vs. Splice Machine vs. SQream DB

System Properties Comparison Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. Sphinx vs. Splice Machine vs. SQream DB

Editorial information provided by DB-Engines
NameMicrosoft Azure Data Explorer  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparisonSQream DB  Xexclude from comparison
DescriptionFully managed big data interactive analytics platformWidely used in-process key-value storeOpen 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 GPU-based, columnar RDBMS for big data analytics workloads
Primary database modelRelational DBMS infocolumn orientedKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Search engineRelational DBMSRelational 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
Score2.01
Rank#126  Overall
#21  Key-value stores
#3  Native XML DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Score0.54
Rank#252  Overall
#115  Relational DBMS
Score0.74
Rank#224  Overall
#103  Relational DBMS
Websiteazure.microsoft.com/­services/­data-explorerwww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlsphinxsearch.comsplicemachine.comsqream.com
Technical documentationdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.oracle.com/­cd/­E17076_05/­html/­index.htmlsphinxsearch.com/­docssplicemachine.com/­how-it-worksdocs.sqream.com
DeveloperMicrosoftOracle infooriginally developed by Sleepycat, which was acquired by OracleSphinx Technologies Inc.Splice MachineSQream Technologies
Initial release20191994200120142017
Current releasecloud service with continuous releases18.1.40, May 20203.5.1, February 20233.1, March 20212022.1.6, December 2022
License infoCommercial or Open SourcecommercialOpen Source infocommercial license availableOpen Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license availablecommercial
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, Java, C++ (depending on the Berkeley DB edition)C++JavaC++, CUDA, Haskell, Java, Scala
Server operating systemshostedAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Linux
Data schemeFixed schema with schema-less datatypes (dynamic)schema-freeyesyesyes
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-typesnonoyesyes, ANSI Standard SQL Types
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.yesyes infoonly with the Berkeley DB XML edition
Secondary indexesall fields are automatically indexedyesyes infofull-text index on all search fieldsyesno
SQL infoSupport of SQLKusto Query Language (KQL), SQL subsetyes infoSQL interfaced based on SQLite is availableSQL-like query language (SphinxQL)yesyes
APIs and other access methodsMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Proprietary protocolJDBC
Native Spark Datasource
ODBC
.Net
JDBC
ODBC
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresYes, possible languages: KQL, Python, Rnonoyes infoJavauser defined functions in Python
Triggersyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoonly for the SQL APInoyesno
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud servicenoneSharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar Partitioninghorizontal and vertical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationnoneMulti-source replication
Source-replica replication
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsSpark connector (open source): github.com/­Azure/­azure-kusto-sparknonoYes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, 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.noyesyes
User concepts infoAccess controlAzure Active Directory AuthenticationnonoAccess rights for users, groups and roles according to SQL-standard

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More resources
Microsoft Azure Data ExplorerOracle Berkeley DBSphinxSplice MachineSQream DB
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