DBMS > BigObject vs. GeoMesa vs. Heroic vs. VelocityDB

System Properties Comparison BigObject vs. GeoMesa vs. Heroic vs. VelocityDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Spatial DBMS

Time Series DBMS

Graph DBMS
Object oriented DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

bigobject.io

www.geomesa.org

github.com/spotify/heroic

velocitydb.com

Technical documentation

docs.bigobject.io

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

spotify.github.io/heroic

velocitydb.com/UserGuide

Developer

BigObject, Inc.

CCRi and others

Spotify

VelocityDB Inc

Initial release

2015

2014

2011

Current release

5.0.0, May 2024

7.x

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache License 2.0

Open Source

Apache 2.0

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

Java

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Any that supports .NET

Data scheme

yes

schema-free

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.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

.Net

Supported programming languages

.Net

Server-side scripts

Stored procedures

Lua

Triggers

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

none

depending on storage layer

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

depending on storage layer

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

depending on storage layer

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

depending on storage layer

yes

User concepts

Access control

yes

depending on the DBMS used for storage

Based on Windows Authentication

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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