DBMS > Datomic vs. Greenplum vs. Sadas Engine vs. STSdb

System Properties Comparison Datomic vs. Greenplum vs. Sadas Engine vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

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

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	1.55
Rank	#144	Overall
	#67	Relational DBMS

Score	7.71
Rank	#46	Overall
	#29	Relational DBMS

Score	0.00
Rank	#385	Overall
	#159	Relational DBMS

Score	0.03
Rank	#365	Overall
	#54	Key-value stores

Website

www.datomic.com

greenplum.org

www.sadasengine.com

github.com/STSSoft/STSdb4

Technical documentation

docs.datomic.com

docs.greenplum.org

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

Developer

Cognitect

Pivotal Software Inc.

SADAS s.r.l.

STS Soft SC

Initial release

2012

2005

2006

2011

Current release

1.0.7180, July 2024

7.0.0, September 2023

8.0

4.0.8, September 2015

License

Commercial or Open Source

commercial

limited edition free

Open Source

Apache 2.0

commercial

free trial version available

Open Source

GPLv2, commercial license available

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

Linux

AIX
Linux
Windows

Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

since Version 4.2

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

JDBC
ODBC

JDBC
ODBC
Proprietary protocol

.NET Client API

Supported programming languages

Clojure
Java

C
Java
Perl
Python
R

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

C#
Java

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

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

fine grained access rights according to SQL-standard

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

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