DBMS > Amazon Redshift vs. Datomic vs. Microsoft Azure Synapse Analytics vs. NSDb vs. VoltDB

System Properties Comparison Amazon Redshift vs. Datomic vs. Microsoft Azure Synapse Analytics vs. NSDb vs. VoltDB

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

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

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Time Series DBMS

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	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

aws.amazon.com/redshift

www.datomic.com

azure.microsoft.com/services/synapse-analytics

nsdb.io

www.voltdb.com

Technical documentation

docs.aws.amazon.com/redshift

docs.datomic.com

docs.microsoft.com/azure/synapse-analytics

nsdb.io/Architecture

docs.voltdb.com

Developer

Amazon (based on PostgreSQL)

Cognitect

Microsoft

VoltDB Inc.

Initial release

2012

2016

2017

2010

Current release

1.0.6735, June 2023

11.3, April 2022

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

Apache Version 2.0

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Java, Clojure

C++

Java, Scala

Java, C++

Server operating systems

hosted

All OS with a Java VM

hosted

Linux
macOS

Linux
OS X

for development

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like query language

yes

only a subset of SQL 99

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

ADO.NET
JDBC
ODBC

gRPC
HTTP REST
WebSocket

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

C#
Java
PHP

Java
Scala

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

Transact SQL

Java

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

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

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

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

yes

Using Apache Lucene

yes

Snapshots and command logging

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

User concepts

Access control

fine grained access rights according to SQL-standard

yes

Users and roles with access to stored procedures

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