DBMS > Apache Jena - TDB vs. EsgynDB vs. H2GIS vs. Microsoft Azure Data Explorer

System Properties Comparison Apache Jena - TDB vs. EsgynDB vs. H2GIS vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines

Name

Description

A RDF storage and query DBMS, shipped as an optional-use component of the Apache Jena framework

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Spatial extension of H2

Fully managed big data interactive analytics platform

Primary database model

column oriented

Secondary database models

Relational DBMS

Document store

If 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

this 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

support 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

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	3.85
Rank	#86	Overall
	#3	RDF stores

Score	0.23
Rank	#319	Overall
	#141	Relational DBMS

Score	0.05
Rank	#372	Overall
	#7	Spatial DBMS

Score	5.16
Rank	#69	Overall
	#37	Relational DBMS

Website

jena.apache.org/documentation/tdb/index.html

www.esgyn.cn

www.h2gis.org

azure.microsoft.com/services/data-explorer

Technical documentation

jena.apache.org/documentation/tdb/index.html

www.h2gis.org/docs/home

docs.microsoft.com/en-us/azure/data-explorer

Developer

Apache Software Foundation

originally developed by HP Labs

Esgyn

CNRS

Microsoft

Initial release

2000

2015

2013

2019

Current release

4.9.0, July 2023

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache License, Version 2.0

commercial

Open Source

LGPL 3.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++, Java

Java

Server operating systems

All OS with a Java VM

Linux

hosted

Data scheme

yes

RDF Schemas

yes

Fixed schema with schema-less datatypes (dynamic)

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

yes

Kusto Query Language (KQL), SQL subset

APIs and other access methods

Fuseki

REST-style SPARQL HTTP Interface
Jena RDF API
RIO

RDF Input/Output

ADO.NET
JDBC
ODBC

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

Supported programming languages

Java

All languages supporting JDBC/ODBC/ADO.Net

Java

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

Server-side scripts

Stored procedures

yes

Java Stored Procedures

yes

based on H2

Yes, possible languages: KQL, Python, R

Triggers

yes

via event handler

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication between multi datacenters

yes

based on H2

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

TDB Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access control via Jena Security

fine grained access rights according to SQL-standard

yes

based on H2

Azure Active Directory Authentication

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