DB-EnginesExtremeDB: mitigate connectivity issues in a DBMSEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > Apache Jena - TDB vs. BaseX vs. Microsoft Azure Data Explorer vs. TimesTen vs. Vitess

System Properties Comparison Apache Jena - TDB vs. BaseX vs. Microsoft Azure Data Explorer vs. TimesTen vs. Vitess

Editorial information provided by DB-Engines
NameApache Jena - TDB  Xexclude from comparisonBaseX  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonTimesTen  Xexclude from comparisonVitess  Xexclude from comparison
DescriptionA RDF storage and query DBMS, shipped as an optional-use component of the Apache Jena frameworkLight-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.Fully managed big data interactive analytics platformIn-Memory RDBMS compatible to OracleScalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelRDF storeNative XML DBMSRelational DBMS infocolumn orientedRelational 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
Document store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.75
Rank#84  Overall
#3  RDF stores
Score1.73
Rank#142  Overall
#4  Native XML DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score1.31
Rank#163  Overall
#74  Relational DBMS
Score0.82
Rank#209  Overall
#97  Relational DBMS
Websitejena.apache.org/­documentation/­tdb/­index.htmlbasex.orgazure.microsoft.com/­services/­data-explorerwww.oracle.com/­database/­technologies/­related/­timesten.htmlvitess.io
Technical documentationjena.apache.org/­documentation/­tdb/­index.htmldocs.basex.orgdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.oracle.com/­database/­timesten-18.1vitess.io/­docs
DeveloperApache Software Foundation infooriginally developed by HP LabsBaseX GmbHMicrosoftOracle, TimesTen Performance Software, HP infooriginally founded in HP Labs it was acquired by Oracle in 2005The Linux Foundation, PlanetScale
Initial release20002007201919982013
Current release4.9.0, July 202310.7, August 2023cloud service with continuous releases11 Release 2 (11.2.2.8.0)15.0.2, December 2022
License infoCommercial or Open SourceOpen Source infoApache License, Version 2.0Open Source infoBSD licensecommercialcommercialOpen Source infoApache Version 2.0, commercial licenses available
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaGo
Server operating systemsAll OS with a Java VMLinux
OS X
Windows
hostedAIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows
Docker
Linux
macOS
Data schemeyes infoRDF Schemasschema-freeFixed schema with schema-less datatypes (dynamic)yesyes
Typing infopredefined data types such as float or dateyesno infoXQuery supports typesyes 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.yesno
Secondary indexesyesyesall fields are automatically indexedyesyes
SQL infoSupport of SQLnonoKusto Query Language (KQL), SQL subsetyesyes infowith proprietary extensions
APIs and other access methodsFuseki infoREST-style SPARQL HTTP Interface
Jena RDF API
RIO infoRDF Input/Output
Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
ADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languagesJavaActionscript
C
C#
Haskell
Java
JavaScript infoNode.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C++
Java
PL/SQL
Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyesyesYes, possible languages: KQL, Python, RPL/SQLyes infoproprietary syntax
Triggersyes infovia event handleryes infovia eventsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynoyes
Partitioning methods infoMethods for storing different data on different nodesnonenoneSharding infoImplicit feature of the cloud servicenoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnonenoneyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Multi-source replication
Source-replica replication
Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate Consistency or Eventual Consistency depending on configurationEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integritynonoyesyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoTDB Transactionsmultiple readers, single writernoACIDACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes infotable locks or row locks depending on storage engine
Durability infoSupport for making data persistentyesyesyesyes infoby means of logfiles and checkpointsyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes
User concepts infoAccess controlAccess control via Jena SecurityUsers with fine-grained authorization concept on 4 levelsAzure Active Directory Authenticationfine grained access rights according to SQL-standardUsers with fine-grained authorization concept infono user groups or roles

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
Apache Jena - TDBBaseXMicrosoft Azure Data ExplorerTimesTenVitess
Recent citations in the news

Sparql Secrets In Jena-Fuseki - DataScienceCentral.com
24 July 2022, Data Science Central

Extract and query knowledge graphs using Apache Jena (SPARQL Engine)
4 December 2019, Towards Data Science

A catalogue with semantic annotations makes multilabel datasets FAIR | Scientific Reports
4 May 2022, Nature.com

MarkLogic Hones Its Triple Store
18 August 2015, Datanami

How relevant is data analytics to businesses today?
21 August 2016, The Sociable

provided by Google News

XML Injection Attacks: What to Know About XPath, XQuery, XXE & More
18 May 2022, Hashed Out by The SSL Store™

9 Skills You Need to Become a Data Engineer
2 November 2022, KDnuggets

provided by Google News

Azure Data Explorer: Log and telemetry analytics benchmark
16 August 2022, Microsoft

Providing modern data transfer and storage service at Microsoft with Microsoft Azure - Inside Track Blog
13 July 2023, microsoft.com

Controlling costs in Azure Data Explorer using down-sampling and aggregation
11 February 2019, Microsoft

Individually great, collectively unmatched: Announcing updates to 3 great Azure Data Services
7 February 2019, Microsoft

Log and Telemetry Analytics Performance Benchmark
16 August 2022, Gigaom

provided by Google News

PlanetScale Unveils Distributed MySQL Database Service Based on Vitess
18 May 2021, Datanami

PlanetScale grabs YouTube-developed open-source tech, promises Vitess DBaaS with on-the-fly schema changes
18 May 2021, The Register

With Vitess 4.0, database vendor matures cloud-native platform
13 November 2019, TechTarget

Massively Scaling MySQL Using Vitess
19 February 2019, InfoQ.com

They scaled YouTube -- now they’ll shard everyone with PlanetScale
13 December 2018, TechCrunch

provided by Google News



Share this page

Featured Products

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

SingleStore logo

The database to transact, analyze and contextualize your data in real time.
Try it today.

RaimaDB logo

RaimaDB, embedded database for mission-critical applications. When performance, footprint and reliability matters.
Try RaimaDB for free.

Present your product here