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DBMS > MarkLogic vs. Microsoft Azure Data Explorer vs. searchxml vs. Vertica

System Properties Comparison MarkLogic vs. Microsoft Azure Data Explorer vs. searchxml vs. Vertica

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Editorial information provided by DB-Engines
NameMarkLogic  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonsearchxml  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionOperational and transactional Enterprise NoSQL databaseFully managed big data interactive analytics platformDBMS for structured and unstructured content wrapped with an application serverCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMS infocolumn orientedNative XML DBMS
Search engine
Relational DBMS infoColumn oriented
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
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score5.18
Rank#63  Overall
#11  Document stores
#1  Native XML DBMS
#1  RDF stores
#7  Search engines
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.03
Rank#390  Overall
#7  Native XML DBMS
#24  Search engines
Score10.06
Rank#42  Overall
#26  Relational DBMS
Websitewww.progress.com/­marklogicazure.microsoft.com/­services/­data-explorerwww.searchxml.net/­category/­productswww.vertica.com
Technical documentationwww.progress.com/­marklogic/­documentationdocs.microsoft.com/­en-us/­azure/­data-explorerwww.searchxml.net/­support/­handoutsvertica.com/­documentation
DeveloperMarkLogic Corp.Microsoftinformationpartners gmbhOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2001201920152005
Current release11.0, December 2022cloud service with continuous releases1.012.0.3, January 2023
License infoCommercial or Open Sourcecommercial inforestricted free version is availablecommercialcommercialcommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenoyesnono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C++C++
Server operating systemsLinux
OS X
Windows
hostedWindowsLinux
Data schemeschema-free infoSchema can be enforcedFixed schema with schema-less datatypes (dynamic)schema-freeYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesyes
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.yesyesyesno
Secondary indexesyesall fields are automatically indexedyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLyes infoSQL92Kusto Query Language (KQL), SQL subsetnoFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJava API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
RESTful HTTP API
WebDAV
XQuery
XSLT
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesC
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C++ infomost other programming languages supported via APIsC#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresyes infovia XQuery or JavaScriptYes, possible languages: KQL, Python, Ryes infoon the application serveryes, PostgreSQL PL/pgSQL, with minor differences
Triggersyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud servicenonehorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes infosychronisation to multiple collectionsMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infocan act as a resource manager in an XA/JTA transactionnomultiple readers, single writerACID
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.yes, with Range Indexesnonono
User concepts infoAccess controlRole-based access control at the document and subdocument levelsAzure Active Directory AuthenticationDomain, group and role-based access control at the document level and for application servicesfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
MarkLogicMicrosoft Azure Data ExplorersearchxmlVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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More resources
MarkLogicMicrosoft Azure Data ExplorersearchxmlVertica infoOpenText™ Vertica™
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