DBMS > Amazon Aurora vs. EXASOL vs. Faircom EDGE vs. Google BigQuery vs. Hazelcast

System Properties Comparison Amazon Aurora vs. EXASOL vs. Faircom EDGE vs. Google BigQuery vs. Hazelcast

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

A widely adopted in-memory data grid

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Key-value store

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	7.84
Rank	#44	Overall
	#28	Relational DBMS

Score	1.66
Rank	#138	Overall
	#64	Relational DBMS

Score	0.02
Rank	#370	Overall
	#55	Key-value stores
	#157	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	5.72
Rank	#59	Overall
	#6	Key-value stores

Website

aws.amazon.com/rds/aurora

www.exasol.com

www.faircom.com/products/faircom-edge

cloud.google.com/bigquery

hazelcast.com

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

www.exasol.com/resources

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

cloud.google.com/bigquery/docs

hazelcast.org/imdg/docs

Developer

Amazon

Exasol

FairCom Corporation

Google

Hazelcast

Initial release

2015

2000

1979

2010

2008

Current release

V3, October 2020

5.3.6, November 2023

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Apache Version 2; commercial licenses 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++

Java

Server operating systems

hosted

Android
Linux

ARM, x86
Raspbian
Windows

hosted

All OS with a Java VM

Data scheme

yes

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

yes

schema-free

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL queries

yes

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
ODBC

.Net
JDBC
ODBC
WebSocket

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

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java
Lua
Python
R

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

yes

user defined functions

yes

.Net, JavaScript, C/C++

user defined functions

in JavaScript

yes

Event Listeners, Executor Services

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

File partitioning

Customizable business rules for partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes

Replicated Map

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

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

one or two-phase-commit; repeatable reads; read commited

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

User concepts

Access control

fine grained access rights according to SQL-standard

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

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)

Role-based access control

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