DBMS > Amazon Redshift vs. Google Cloud Datastore vs. Microsoft Azure Synapse Analytics vs. NSDb vs. RethinkDB

System Properties Comparison Amazon Redshift vs. Google Cloud Datastore vs. Microsoft Azure Synapse Analytics vs. NSDb vs. RethinkDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Primary database model

Secondary database models

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Website

aws.amazon.com/redshift

cloud.google.com/datastore

azure.microsoft.com/services/synapse-analytics

nsdb.io

rethinkdb.com

Technical documentation

docs.aws.amazon.com/redshift

cloud.google.com/datastore/docs

docs.microsoft.com/azure/synapse-analytics

nsdb.io/Architecture

rethinkdb.com/docs

Developer

Amazon (based on PostgreSQL)

Google

Microsoft

The Linux Foundation

since July 2017

Initial release

2012

2008

2016

2017

2009

Current release

2.4.1, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2

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++

Java, Scala

C++

Server operating systems

hosted

Linux
macOS

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

yes

yes: int, bigint, decimal, string

yes

string, binary, float, bool, date, geometry

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

all fields are automatically indexed

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language (GQL)

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

gRPC
HTTP REST
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
Java
PHP

Java
Scala

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Server-side scripts

Stored procedures

user defined functions

in Python

using Google App Engine

Transact SQL

Triggers

Callbacks using the Google Apps Engine

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

range based

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

via ReferenceProperties or Ancestor paths

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

Durability

Support for making data persistent

yes

Using Apache Lucene

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 based on Google Cloud Identity and Access Management (IAM)

yes

users and table-level permissions

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