DBMS > Amazon Redshift vs. Apache IoTDB vs. Firebase Realtime Database vs. GigaSpaces

System Properties Comparison Amazon Redshift vs. Apache IoTDB vs. Firebase Realtime Database vs. GigaSpaces

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Primary database model

Relational DBMS

Time Series DBMS

Document store

Document store
Object oriented DBMS

Values are user defined objects

Secondary database models

Graph DBMS
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Trend Chart

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	13.60
Rank	#39	Overall
	#6	Document stores

Score	0.91
Rank	#197	Overall
	#33	Document stores
	#7	Object oriented DBMS

Website

aws.amazon.com/redshift

iotdb.apache.org

firebase.google.com/products/realtime-database

www.gigaspaces.com

Technical documentation

docs.aws.amazon.com/redshift

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

firebase.google.com/docs/database

docs.gigaspaces.com/latest/landing.html

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Google

acquired by Google 2014

Gigaspaces Technologies

Initial release

2012

2018

2012

2000

Current release

1.1.0, April 2023

15.5, September 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

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

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Implementation language

Java

Java, C++, .Net

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

hosted

Linux
macOS
Solaris
Windows

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.

XML can be used for describing objects metadata

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

SQL-99 for query and DML statements

APIs and other access methods

JDBC
ODBC

JDBC
Native API

Android
iOS
JavaScript API
RESTful HTTP API

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Go
Java
Python
Scala

Java
JavaScript
Objective-C

.Net
C++
Java
Python
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

limited functionality with using 'rules'

yes

Triggers

yes

Callbacks are triggered when data changes

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

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

yes

yes, based on authentication and database rules

Role-based access control

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