DBMS > GeoMesa vs. Microsoft Azure Table Storage vs. ObjectBox vs. RDF4J

System Properties Comparison GeoMesa vs. Microsoft Azure Table Storage vs. ObjectBox vs. RDF4J

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

A Wide Column Store for rapid development using massive semi-structured datasets

Extremely fast embedded database for small devices, IoT and Mobile

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

Primary database model

Secondary database models

Time Series DBMS

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

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

www.geomesa.org

azure.microsoft.com/en-us/services/storage/tables

objectbox.io

rdf4j.org

Technical documentation

www.geomesa.org/documentation/stable/user/index.html

docs.objectbox.io

rdf4j.org/documentation

Developer

CCRi and others

Microsoft

ObjectBox Limited

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

Initial release

2014

2012

2017

2004

Current release

5.0.0, May 2024

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

Apache License 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

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

Scala

C and C++

Java

Server operating systems

hosted

Android
iOS
Linux
macOS
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

yes

SQL

Support of SQL

APIs and other access methods

RESTful HTTP API

Proprietary native API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

Java
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

yes

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

online/offline synchronization between client and server

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

optimistic locking

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities

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

depending on storage layer

User concepts

Access control

yes

depending on the DBMS used for storage

Access rights based on private key authentication or shared access signatures

yes

More information provided by the system vendor

News

Evolution of search: traditional vs vector search
23 May 2024

On-device Vector Database for Dart/Flutter
21 May 2024

The first On-Device Vector Database: ObjectBox 4.0
16 May 2024

SQLite and SQLite alternatives – a comprehensive overview
7 May 2024

Edge AI: The era of on-device AI
23 April 2024

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

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

show all

Recent citations in the news

Working with Azure to Use and Manage Data Lakes
7 March 2024, Simplilearn

Azure Cosmos DB Data Migration tool imports from Azure Table storage | Azure updates
5 May 2015, Microsoft

How to Use C# Azure.Data.Tables SDK with Azure Cosmos DB
9 July 2021, hackernoon.com

How to use Azure Table storage in .Net
14 January 2019, InfoWorld

How to write data to Azure Table Store with an Azure Function
14 April 2017, Experts Exchange

provided by Google News

Industrial IoT (IIoT) edge solution for railway operators – a Kapsch ObjectBox Case Study
21 June 2019, 1E9

The Megashift Towards Decentralized Edge Computing
27 August 2021, hackernoon.com

provided by Google News

GraphDB Goes Open Source
27 January 2020, iProgrammer

Ontotext's GraphDB 8.10 Makes Knowledge Graph Experience Faster and Richer
13 June 2019, Markets Insider

provided by Google News

Share this page