DBMS > Amazon Redshift vs. BigObject vs. Faircom EDGE vs. HEAVY.AI

System Properties Comparison Amazon Redshift vs. BigObject vs. Faircom EDGE vs. HEAVY.AI

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Website

aws.amazon.com/redshift

bigobject.io

www.faircom.com/products/faircom-edge

github.com/heavyai/heavydb
www.heavy.ai

Technical documentation

docs.aws.amazon.com/redshift

docs.bigobject.io

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

docs.heavy.ai

Developer

Amazon (based on PostgreSQL)

BigObject, Inc.

FairCom Corporation

HEAVY.AI, Inc.

Initial release

2012

2015

1979

2016

Current release

V3, October 2020

5.10, January 2022

License

Commercial or Open Source

commercial

free community edition available

commercial

Restricted, free version available

Open Source

Apache Version 2; enterprise edition available

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++

C++ and CUDA

Server operating systems

hosted

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

Data scheme

yes

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

yes

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

ANSI SQL queries

yes

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

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

JDBC
ODBC
Thrift
Vega

Supported programming languages

All languages supporting JDBC/ODBC

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

All languages supporting JDBC/ODBC/Thrift
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

yes

.Net, JavaScript, C/C++

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

File partitioning

Customizable business rules for partitioning

Sharding

Round robin

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

automatically between fact table and dimension tables

yes

when using SQL

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

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

User concepts

Access control

fine grained access rights according to SQL-standard

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

fine grained access rights according to SQL-standard

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