DBMS > BigObject vs. Faircom EDGE vs. YugabyteDB

System Properties Comparison BigObject vs. Faircom EDGE vs. YugabyteDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

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

High-performance distributed SQL database for global, internet-scale applications. Wire and feature compatible with PostgreSQL.

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Wide column store

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

Score	0.13
Rank	#333	Overall
	#148	Relational DBMS

Score	0.01
Rank	#384	Overall
	#59	Key-value stores
	#157	Relational DBMS

Score	2.45
Rank	#107	Overall
	#53	Relational DBMS

Website

bigobject.io

www.faircom.com/products/faircom-edge

www.yugabyte.com

Technical documentation

docs.bigobject.io

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

docs.yugabyte.com
github.com/yugabyte/yugabyte-db

Developer

BigObject, Inc.

FairCom Corporation

Yugabyte Inc.

Initial release

2015

1979

2017

Current release

V3, October 2020

2.19, September 2023

License

Commercial or Open Source

commercial

free community edition available

commercial

Restricted, free version available

Open Source

Apache 2.0

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

ANSI C, C++

C and C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Android
Linux

ARM, x86
Raspbian
Windows

Linux
OS X

Data scheme

yes

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

depending on used data model

Typing

predefined data types such as float or date

yes

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

SQL-like DML and DDL statements

yes

ANSI SQL queries

yes, PostgreSQL compatible

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

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

JDBC
YCQL, an SQL-based flexible-schema API with its roots in Cassandra Query Language
YSQL - a fully relational SQL API that is wire compatible with the SQL language in PostgreSQL

Supported programming languages

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

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust
Scala

Server-side scripts

Stored procedures

Lua

yes

.Net, JavaScript, C/C++

yes

sql, plpgsql, C

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

File partitioning

Customizable business rules for partitioning

Hash and Range Sharding, row-level geo-partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Synchronous and asynchronous realtime replication based on transaction logs

Based on Raft distributed consensus protocol, minimum 3 replicas for continuous availability

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency
Tunable Consistency

Strong consistency on writes and tunable consistency on reads

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

when using SQL

yes

Transaction concepts

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

ACID

Distributed ACID with Serializable & Snapshot Isolation. Inspired by Google Spanner architecture.

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

based on RocksDB

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.

yes

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