DBMS > Amazon Redshift vs. Apache IoTDB vs. eXtremeDB vs. MaxDB vs. TimesTen

System Properties Comparison Amazon Redshift vs. Apache IoTDB vs. eXtremeDB vs. MaxDB vs. TimesTen

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

Natively in-memory DBMS with options for persistency, high-availability and clustering

A robust and reliable RDBMS optimized to run all major SAP solutions

In-Memory RDBMS compatible to Oracle

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.80
Rank	#214	Overall
	#99	Relational DBMS
	#18	Time Series DBMS

Score	2.26
Rank	#114	Overall
	#55	Relational DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

aws.amazon.com/redshift

iotdb.apache.org

www.mcobject.com

maxdb.sap.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.aws.amazon.com/redshift

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

www.mcobject.com/docs/extremedb.htm

maxdb.sap.com/documentation

docs.oracle.com/database/timesten-18.1

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

McObject

SAP, acquired from Software AG (Adabas-D) in 1997

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2012

2018

2001

1984

1998

Current release

1.1.0, April 2023

8.2, 2021

7.9.10.12, February 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Limited community edition free

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

C and C++

C++

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

AIX
HP-UX
Linux
macOS
Solaris
Windows

AIX
HP-UX
Linux
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

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.

support of XML interfaces available

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

yes

with the option: eXtremeSQL

yes

APIs and other access methods

JDBC
ODBC

JDBC
Native API

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
WebDAV

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Go
Java
Python
Scala

.Net
C
C#
C++
Java
Lua
Python
Scala

.Net
C#
Java
Perl
PHP
Python

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions

in Python

yes

PL/SQL

Triggers

yes

by defining events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

horizontal partitioning / sharding

none

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

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

Source-replica replication

Multi-source replication
Source-replica replication

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

Immediate Consistency or Eventual Consistency depending on configuration

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

Optimistic (MVCC) and pessimistic (locking) strategies available

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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