DBMS > Faircom EDGE vs. HEAVY.AI vs. Microsoft Azure AI Search vs. Sadas Engine vs. Snowflake

System Properties Comparison Faircom EDGE vs. HEAVY.AI vs. Microsoft Azure AI Search vs. Sadas Engine vs. Snowflake

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

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

Search-as-a-service for web and mobile app development

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Search engine

Relational DBMS

Secondary database models

Spatial DBMS

Vector 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	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

www.faircom.com/products/faircom-edge

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

azure.microsoft.com/en-us/services/search

www.sadasengine.com

www.snowflake.com

Technical documentation

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

docs.heavy.ai

learn.microsoft.com/en-us/azure/search

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.snowflake.net/manuals/index.html

Developer

FairCom Corporation

HEAVY.AI, Inc.

Microsoft

SADAS s.r.l.

Snowflake Computing Inc.

Initial release

1979

2016

2015

2006

2014

Current release

V3, October 2020

5.10, January 2022

8.0

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache Version 2; enterprise edition available

commercial

free trial version available

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++

C++ and CUDA

C++

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Linux

hosted

AIX
Linux
Windows

hosted

Data scheme

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

yes

support of semi-structured data formats (JSON, XML, Avro)

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

yes

APIs and other access methods

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

JDBC
ODBC
Thrift
Vega

RESTful HTTP API

JDBC
ODBC
Proprietary protocol

CLI Client
JDBC
ODBC

Supported programming languages

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

All languages supporting JDBC/ODBC/Thrift
Python

C#
Java
JavaScript
Python

.Net
C
C#
C++
Groovy
Java
PHP
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Sharding

Round robin

Sharding

Implicit feature of the cloud service

horizontal partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication

yes

Implicit feature of the cloud service

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

ACID

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

managed by 'Learn by Usage'

User concepts

Access control

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

fine grained access rights according to SQL-standard

yes

using Azure authentication

Access rights for users, groups and roles according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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