DBMS > Faircom EDGE vs. FatDB vs. GeoMesa vs. Google BigQuery

System Properties Comparison Faircom EDGE vs. FatDB vs. GeoMesa vs. Google BigQuery

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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

Large scale data warehouse service with append-only tables

Primary database model

Key-value store
Relational DBMS

Document store
Key-value store

Spatial DBMS

Relational DBMS

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

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Website

www.faircom.com/products/faircom-edge

www.geomesa.org

cloud.google.com/bigquery

Technical documentation

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

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

cloud.google.com/bigquery/docs

Developer

FairCom Corporation

FatCloud

CCRi and others

Google

Initial release

1979

2012

2014

2010

Current release

V3, October 2020

5.0.0, May 2024

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Apache License 2.0

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

ANSI C, C++

Scala

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Windows

hosted

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

schema-free

yes

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

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

yes

ANSI SQL queries

Via inetgration in SQL Server

yes

APIs and other access methods

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

via applications

user defined functions

in JavaScript

Triggers

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Sharding

depending on storage layer

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

selectable replication factor

depending on storage layer

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Eventual Consistency
Immediate Consistency

depending on storage layer

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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

User concepts

Access control

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Can implement custom security layer via applications

yes

depending on the DBMS used for storage

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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