DBMS > Amazon Redshift vs. IBM Db2 warehouse vs. NSDb

System Properties Comparison Amazon Redshift vs. IBM Db2 warehouse vs. NSDb

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Cloud-based data warehousing service

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

Primary database model

Relational DBMS

Time Series DBMS

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	1.34
Rank	#166	Overall
	#76	Relational DBMS

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Website

aws.amazon.com/redshift

www.ibm.com/products/db2/warehouse

nsdb.io

Technical documentation

docs.aws.amazon.com/redshift

nsdb.io/Architecture

Developer

Amazon (based on PostgreSQL)

IBM

Initial release

2012

2014

2017

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

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, Scala

Server operating systems

hosted

Linux
macOS

Data scheme

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.

Import/export of XML data possible

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
JDBC
ODBC
OLE DB

gRPC
HTTP REST
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Java
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

PL/SQL, SQL PL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

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