DBMS > Datomic vs. Sadas Engine vs. Teradata Aster

System Properties Comparison Datomic vs. Sadas Engine vs. Teradata Aster

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Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

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

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Platform for big data analytics on multistructured data sources and types

Primary database model

Relational DBMS

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

www.datomic.com

www.sadasengine.com

Technical documentation

docs.datomic.com

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Cognitect

SADAS s.r.l.

Teradata

Initial release

2012

2006

2005

Current release

1.0.6735, June 2023

8.0

License

Commercial or Open Source

commercial

limited edition free

commercial

free trial version available

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, Clojure

C++

Server operating systems

All OS with a Java VM

AIX
Linux
Windows

Linux

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
Proprietary protocol

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

Clojure
Java

.Net
C
C#
C++
Groovy
Java
PHP
Python

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

Transaction Functions

R packages

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

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

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

yes

managed by 'Learn by Usage'

User concepts

Access control

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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