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DBMS > Apache Druid vs. Microsoft Azure Data Explorer vs. TerminusDB vs. Virtuoso

System Properties Comparison Apache Druid vs. Microsoft Azure Data Explorer vs. TerminusDB vs. Virtuoso

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonTerminusDB infoformer name was DataChemist  Xexclude from comparisonVirtuoso  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataFully managed big data interactive analytics platformScalable Graph Database platform making enterprise data available by exploiting inferred entities and relationshipsVirtuoso is a multi-model hybrid-RDBMS that supports management of data represented as relational tables and/or property graphs
Primary database modelRelational DBMS
Time Series DBMS
Relational DBMS infocolumn orientedGraph DBMSDocument store
Graph DBMS
Native XML DBMS
Relational DBMS
RDF store
Search engine
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
Document store
RDF store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.25
Rank#90  Overall
#47  Relational DBMS
#7  Time Series DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.23
Rank#316  Overall
#27  Graph DBMS
Score4.27
Rank#73  Overall
#13  Document stores
#4  Graph DBMS
#2  Native XML DBMS
#39  Relational DBMS
#2  RDF stores
#9  Search engines
Websitedruid.apache.orgazure.microsoft.com/­services/­data-explorerterminusdb.comvirtuoso.openlinksw.com
Technical documentationdruid.apache.org/­docs/­latest/­designdocs.microsoft.com/­en-us/­azure/­data-explorerterminusdb.github.io/­terminusdb/­#docs.openlinksw.com/­virtuoso
DeveloperApache Software Foundation and contributorsMicrosoftDataChemist Ltd.OpenLink Software
Initial release2012201920181998
Current release29.0.1, April 2024cloud service with continuous releases11.0.0, January 20237.2.11, September 2023
License infoCommercial or Open SourceOpen Source infoApache license v2commercialOpen Source infoGPL V3Open Source infoGPLv2, extended commercial license available
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaProlog, RustC
Server operating systemsLinux
OS X
Unix
hostedLinuxAIX
FreeBSD
HP-UX
Linux
OS X
Solaris
Windows
Data schemeyes infoschema-less columns are supportedFixed schema with schema-less datatypes (dynamic)yesyes infoSQL - Standard relational schema
RDF - Quad (S, P, O, G) or Triple (S, P, O)
XML - DTD, XML Schema
DAV - freeform filesystem objects, plus User Defined Types a/k/a Dynamic Extension Type
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.noyesnoyes
Secondary indexesyesall fields are automatically indexedyes
SQL infoSupport of SQLSQL for queryingKusto Query Language (KQL), SQL subsetSQL-like query language (WOQL)yes infoSQL-92, SQL-200x, SQL-3, SQLX
APIs and other access methodsJDBC
RESTful HTTP/JSON API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
OWL
RESTful HTTP API
WOQL (Web Object Query Language)
ADO.NET
GeoSPARQL
HTTP API
JDBC
Jena RDF API
ODBC
OLE DB
RDF4J API
RESTful HTTP API
Sesame REST HTTP Protocol
SOAP webservices
SPARQL 1.1
WebDAV
XPath
XQuery
XSLT
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
JavaScript
Python
.Net
C
C#
C++
Java
JavaScript
Perl
PHP
Python
Ruby
Visual Basic
Server-side scripts infoStored proceduresnoYes, possible languages: KQL, Python, Ryesyes infoVirtuoso PL
Triggersnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesyes
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedSharding infoImplicit feature of the cloud serviceGraph Partitioningyes
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Journaling StreamsChain, star, and bi-directional replication
Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoin-memory journalingyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemAzure Active Directory AuthenticationRole-based access controlFine-grained Attribute-Based Access Control (ABAC) in addition to typical coarse-grained Role-Based Access Control (RBAC) according to SQL-standard. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)
More information provided by the system vendor
Apache DruidMicrosoft Azure Data ExplorerTerminusDB infoformer name was DataChemistVirtuoso
Specific characteristicsVirtuoso is a modern multi-model RDBMS for managing data represented as tabular relations...
» more
Competitive advantagesPerformance & Scale — as exemplified by DBpedia and the LOD Cloud it spawned, i.e.,...
» more
Typical application scenariosUsed for — Analytics/BI Conceptual Data Virtualization Enterprise Knowledge Graphs...
» more
Key customersBroad use across enterprises and governments including — European Union (EU) US Government...
» more
Market metricsLargest installed-base ​of Multi-Model RDBMS for AI-friendly Knowledge Graphs Platform...
» more
Licensing and pricing modelsAvailable in both Commercial Enterprise and Open Source (GPL v2) Editions Feature...
» more

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Apache DruidMicrosoft Azure Data ExplorerTerminusDB infoformer name was DataChemistVirtuoso
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