DBMS > Amazon Redshift vs. Apache IoTDB vs. Datomic vs. IBM Db2 vs. Microsoft Azure AI Search

System Properties Comparison Amazon Redshift vs. Apache IoTDB vs. Datomic vs. IBM Db2 vs. Microsoft Azure AI Search

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Common in IBM host environments, 2 different versions for host and Windows/Linux

Search-as-a-service for web and mobile app development

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Search engine

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Vector DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Website

aws.amazon.com/redshift

iotdb.apache.org

www.datomic.com

www.ibm.com/products/db2

azure.microsoft.com/en-us/services/search

Technical documentation

docs.aws.amazon.com/redshift

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.datomic.com

www.ibm.com/docs/en/db2

learn.microsoft.com/en-us/azure/search

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Cognitect

IBM

Microsoft

Initial release

2012

2018

2012

1983

host version

2015

Current release

1.1.0, April 2023

1.0.6735, June 2023

12.1, October 2016

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

limited edition free

commercial

free version is available

commercial

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

Java, Clojure

C and C++

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

All OS with a Java VM

AIX
HP-UX
Linux
Solaris
Windows
z/OS

hosted

Data scheme

yes

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

JDBC
Native API

RESTful HTTP API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Go
Java
Python
Scala

Clojure
Java

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

C#
Java
JavaScript
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

yes

Triggers

yes

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

none

But extensive use of caching in the application peers

Sharding

only with Windows/Unix/Linux Version

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

none

But extensive use of caching in the application peers

yes

with separate tools (MQ, InfoSphere)

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

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

yes

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

fine grained access rights according to SQL-standard

yes

using Azure authentication

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