DBMS > H2GIS vs. Microsoft Azure Data Explorer vs. Prometheus vs. RDF4J vs. SQream DB

System Properties Comparison H2GIS vs. Microsoft Azure Data Explorer vs. Prometheus vs. RDF4J vs. SQream DB

Editorial information provided by DB-Engines

Name

Description

Spatial extension of H2

Fully managed big data interactive analytics platform

Open-source Time Series DBMS and monitoring system

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

a GPU-based, columnar RDBMS for big data analytics workloads

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	0.08
Rank	#368	Overall
	#7	Spatial DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

www.h2gis.org

azure.microsoft.com/services/data-explorer

prometheus.io

rdf4j.org

sqream.com

Technical documentation

www.h2gis.org/docs/home

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

prometheus.io/docs

rdf4j.org/documentation

docs.sqream.com

Developer

CNRS

Microsoft

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

SQream Technologies

Initial release

2013

2019

2015

2004

2017

Current release

cloud service with continuous releases

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

LGPL 3.0

commercial

Open Source

Apache 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

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++, CUDA, Haskell, Java, Scala

Server operating systems

hosted

Linux
Windows

Linux
OS X
Unix
Windows

Linux

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

RDF Schemas

yes

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

Numeric data only

yes

yes, ANSI Standard SQL 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

Import of XML data possible

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

Kusto Query Language (KQL), SQL subset

yes

APIs and other access methods

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

RESTful HTTP/JSON API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

.Net
JDBC
ODBC

Supported programming languages

Java

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

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Java
PHP
Python

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

based on H2

Yes, possible languages: KQL, Python, R

yes

user defined functions in Python

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

none

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on H2

yes

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

yes

by Federation

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

yes

based on H2

Azure Active Directory Authentication

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