DBMS > OpenMLDB vs. Sadas Engine vs. SAP HANA vs. VoltDB

System Properties Comparison OpenMLDB vs. Sadas Engine vs. SAP HANA vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

An open-source machine learning database that provides a feature platform for training and inference

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

In-memory, column based data store. Available as appliance or cloud service

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

openmldb.ai

www.sadasengine.com

www.sap.com/products/hana.html

www.voltdb.com

Technical documentation

openmldb.ai/docs/zh/main

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

help.sap.com/hana

docs.voltdb.com

Developer

4 Paradigm Inc.

SADAS s.r.l.

SAP

VoltDB Inc.

Initial release

2020

2006

2010

Current release

2024-2 February 2024

8.0

2.0 SPS07 (April 4, 2023), April 2023

11.3, April 2022

License

Commercial or Open Source

Open Source

commercial

free trial version available

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++, Java, Scala

C++

Java, C++

Server operating systems

Linux

AIX
Linux
Windows

Appliance or cloud-service

Linux
OS X

for development

Data scheme

Fixed schema

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.

Secondary indexes

yes

SQL

Support of SQL

yes

only a subset of SQL 99

APIs and other access methods

JDBC
SQLAlchemy

JDBC
ODBC
Proprietary protocol

JDBC
ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C++
Go
Java
Python
Scala

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

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

SQLScript, R

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

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

yes

managed by 'Learn by Usage'

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

yes

Users and roles with access to stored procedures

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