DBMS > Amazon Redshift vs. BigObject vs. EXASOL vs. Google Cloud Firestore vs. Oracle Coherence

System Properties Comparison Amazon Redshift vs. BigObject vs. EXASOL vs. Google Cloud Firestore vs. Oracle Coherence

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

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

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Oracles in-memory data grid solution

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Document store

Key-value store

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	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Website

aws.amazon.com/redshift

bigobject.io

www.exasol.com

firebase.google.com/products/firestore

www.oracle.com/java/coherence

Technical documentation

docs.aws.amazon.com/redshift

docs.bigobject.io

www.exasol.com/resources

firebase.google.com/docs/firestore

docs.oracle.com/en/middleware/standalone/coherence

Developer

Amazon (based on PostgreSQL)

BigObject, Inc.

Exasol

Google

Oracle

Initial release

2012

2015

2000

2017

2007

Current release

14.1, August 2023

License

Commercial or Open Source

commercial

free community edition available

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

Java

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)

hosted

All OS with a Java VM

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

JCache
JPA
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Java
Lua
Python
R

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

.Net
C++
Java

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

user defined functions

yes, Firebase Rules & Cloud Functions

Triggers

yes

yes, with Cloud Functions

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

yes

configurable

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

optionally

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

authentification to access the cache via certificates or http basic authentication

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