DBMS > OushuDB vs. Teradata vs. Yaacomo

System Properties Comparison OushuDB vs. Teradata vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

A hybrid cloud data analytics software platform (Teradata Vantage)

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational 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	0.04
Rank	#363	Overall
	#154	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

www.oushu.com/product/oushuDB

www.teradata.com

yaacomo.com

Technical documentation

www.oushu.com/documentation

docs.teradata.com

Developer

Oushu

Teradata

Q2WEB GmbH

Initial release

1984

2009

Current release

4.0.1, August 2020

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Server operating systems

Linux

hosted
Linux

Android
Linux
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.

yes

Secondary indexes

yes

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

yes

SQL

Support of SQL

Full-featured ANSI SQL support

yes

SQL 2016 + extensions

yes

APIs and other access methods

JDBC
ODBC

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

JDBC
ODBC

Supported programming languages

C
C++

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

Server-side scripts

Stored procedures

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

Hashing

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Kerberos, SSL and role based access

fine grained access rights according to SQL-standard

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More resources

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