DBMS > Amazon Redshift vs. Google Cloud Firestore vs. Kinetica vs. Snowflake vs. Stardog

System Properties Comparison Amazon Redshift vs. Google Cloud Firestore vs. Kinetica vs. Snowflake vs. Stardog

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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.

Fully vectorized database across both GPUs and CPUs

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

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

aws.amazon.com/redshift

firebase.google.com/products/firestore

www.kinetica.com

www.snowflake.com

www.stardog.com

Technical documentation

docs.aws.amazon.com/redshift

firebase.google.com/docs/firestore

docs.kinetica.com

docs.snowflake.net/manuals/index.html

docs.stardog.com

Developer

Amazon (based on PostgreSQL)

Google

Kinetica

Snowflake Computing Inc.

Stardog-Union

Initial release

2012

2017

2012

2014

2010

Current release

7.1, August 2021

7.3.0, May 2020

License

Commercial or Open Source

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

C, C++

Java

Server operating systems

hosted

Linux

hosted

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

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

schema-free and OWL/RDFS-schema support

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.

yes

Import/export of XML data possible

Secondary indexes

restricted

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JDBC
ODBC

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

JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

All languages supporting JDBC/ODBC

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

C++
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

yes, Firebase Rules & Cloud Functions

user defined functions

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes, with Cloud Functions

yes

triggers when inserted values for one or more columns fall within a specified range

similar concept for controling cloud resources

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

Source-replica replication

yes

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

relationships in graphs

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

GPU vRAM or System RAM

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.

Access rights for users and roles on table level

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

Access rights for users and roles

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