DBMS > Amazon Redshift vs. Datomic vs. Google Cloud Firestore vs. SpaceTime vs. Splice Machine

System Properties Comparison Amazon Redshift vs. Datomic vs. Google Cloud Firestore vs. SpaceTime vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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.

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Secondary database models

Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

aws.amazon.com/redshift

www.datomic.com

firebase.google.com/products/firestore

www.mireo.com/spacetime

splicemachine.com

Technical documentation

docs.aws.amazon.com/redshift

docs.datomic.com

firebase.google.com/docs/firestore

splicemachine.com/how-it-works

Developer

Amazon (based on PostgreSQL)

Cognitect

Google

Mireo

Splice Machine

Initial release

2012

2017

2020

2014

Current release

1.0.6735, June 2023

3.1, March 2021

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

AGPL 3.0, commercial license 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, Clojure

C++

Java

Server operating systems

hosted

All OS with a Java VM

hosted

Linux

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

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

A subset of ANSI SQL is implemented

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

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

RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

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

C#
C++
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

yes, Firebase Rules & Cloud Functions

yes

Java

Triggers

By using transaction functions

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding

Fixed-grid hypercubes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

Multi-source replication

Real-time block device replication (DRBD)

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights 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.

yes

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

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