DBMS > Amazon Redshift vs. DataFS vs. Derby vs. EventStoreDB vs. NSDb

System Properties Comparison Amazon Redshift vs. DataFS vs. Derby vs. EventStoreDB vs. NSDb

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Industrial-strength, open-source database solution built from the ground up for event sourcing.

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

Primary database model

Secondary database models

Graph DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	4.60
Rank	#70	Overall
	#38	Relational DBMS

Score	1.19
Rank	#173	Overall
	#1	Event Stores

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

dev.mobiland.com/Overview.xsp

db.apache.org/derby/manuals/index.html

developers.eventstore.com

nsdb.io/Architecture

Developer

Amazon (based on PostgreSQL)

Mobiland AG

Apache Software Foundation

Event Store Limited

Initial release

2012

2018

1997

2012

2017

Current release

1.1.263, October 2022

10.17.1.0, November 2023

21.2, February 2021

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Apache Version 2.0

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, Scala

Server operating systems

hosted

Windows

All OS with a Java VM

Linux
Windows

Linux
macOS

Data scheme

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

Secondary indexes

restricted

yes

all fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC

.NET Client API
Proprietary client DLL
WinRT client

JDBC

gRPC
HTTP REST
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C#
C++
VB.Net

Java

Java
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

Java Stored Procedures

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Proprietary Sharding system

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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

Windows-Profile

fine grained access rights according to SQL-standard

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