DBMS > Apache IoTDB vs. MaxDB vs. Teradata vs. TimesTen vs. Ultipa

System Properties Comparison Apache IoTDB vs. MaxDB vs. Teradata vs. TimesTen vs. Ultipa

Editorial information provided by DB-Engines

Name

Description

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

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

A hybrid cloud data analytics software platform (Teradata Vantage)

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Time Series DBMS

Relational DBMS

Graph DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	2.15
Rank	#115	Overall
	#55	Relational DBMS

Score	41.47
Rank	#22	Overall
	#15	Relational DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Score	0.11
Rank	#338	Overall
	#32	Graph DBMS

Website

iotdb.apache.org

maxdb.sap.com

www.teradata.com

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

www.ultipa.com

Technical documentation

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

maxdb.sap.com/documentation

docs.teradata.com

docs.oracle.com/en/database/other-databases/timesten/index.html

www.ultipa.com/document

Developer

Apache Software Foundation

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

Teradata

Oracle

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

Ultipa

Initial release

2018

1984

1998

2019

Current release

1.1.0, April 2023

7.9.10.12, February 2024

Teradata Vantage 1.0 MU2, January 2019

Release 22.1

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Limited community edition free

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

AIX
HP-UX
Linux
Solaris
Windows

hosted
Linux

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

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.

yes

Secondary indexes

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

yes

SQL

Support of SQL

SQL-like query language

yes

SQL 2016 + extensions

yes

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
ODBC
WebDAV

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C#
Java
Perl
PHP
Python

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

C
C++
Java
Node.js
PL/SQL
Python

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

UDFs, stored procedures, table functions in parallel

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

Hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

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

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

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

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

yes

fine grained access rights according to SQL-standard

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