DBMS > GeoMesa vs. Splunk vs. Ultipa

System Properties Comparison GeoMesa vs. Splunk vs. Ultipa

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Editorial information provided by DB-Engines

Name

Description

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

Analytics Platform for Big Data

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Spatial DBMS

Search engine

Graph DBMS

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

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	86.45
Rank	#14	Overall
	#2	Search engines

Score	0.13
Rank	#335	Overall
	#31	Graph DBMS

Website

www.geomesa.org

www.splunk.com

www.ultipa.com

Technical documentation

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

docs.splunk.com/Documentation/Splunk

www.ultipa.com/document

Developer

CCRi and others

Splunk Inc.

Ultipa

Initial release

2014

2003

2019

Current release

4.0.5, February 2024

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Limited free edition and free developer edition available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Scala

Server operating systems

Linux
OS X
Solaris
Windows

Data scheme

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

Splunk Search Processing Language for search commands

APIs and other access methods

HTTP REST

RESTful HTTP API

Supported programming languages

C#
Java
JavaScript
PHP
Python
Ruby

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

Multi-source replication

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

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

Concurrency

Support for concurrent manipulation of data

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.

depending on storage layer

User concepts

Access control

yes

depending on the DBMS used for storage

Access rights for users and roles

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More resources

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