DBMS > Faircom EDGE vs. Google BigQuery vs. PouchDB vs. SwayDB vs. UniData,UniVerse

System Properties Comparison Faircom EDGE vs. Google BigQuery vs. PouchDB vs. SwayDB vs. UniData,UniVerse

Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

JavaScript DBMS with an API inspired by CouchDB

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

MultiValue database and application server with SQL mapping layer and meta database capabilities

Primary database model

Key-value store
Relational DBMS

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Trend Chart

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Score	3.16
Rank	#97	Overall
	#2	Multivalue DBMS

Website

www.faircom.com/products/faircom-edge

cloud.google.com/bigquery

pouchdb.com

swaydb.simer.au

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-unidata

Technical documentation

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

cloud.google.com/bigquery/docs

pouchdb.com/guides

docs.rocketsoftware.com/bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata

Developer

FairCom Corporation

Google

Apache Software Foundation

Simer Plaha

Rocket Software

Initial release

1979

2010

2012

2018

1985

Current release

V3, October 2020

7.1.1, June 2019

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

GNU Affero GPL V3.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

ANSI C, C++

JavaScript

Scala

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

hosted

server-less, requires a JavaScript environment (browser, Node.js)

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

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

yes

schema-free

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

yes

optional

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

via views

yes

SQL

Support of SQL

yes

ANSI SQL queries

yes

APIs and other access methods

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

RESTful HTTP/JSON API

HTTP REST

only for PouchDB Server
JavaScript API

Java API

JPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript

Java
Kotlin
Scala

.Net
Basic

U2 Basic
C
Java

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

in JavaScript

View functions in JavaScript

yes

Triggers

yes

U2 Basic

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

none

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

Source-replica replication

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

Immediate Consistency

Eventual Consistency

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

Atomic execution of operations

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

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

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

Google Cloud Identity & Access Management (IAM)

Access rights according to SQL-standard and operating system based

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