DBMS > EsgynDB vs. Sadas Engine vs. Spark SQL vs. VelocityDB

System Properties Comparison EsgynDB vs. Sadas Engine vs. Spark SQL vs. VelocityDB

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Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Relational DBMS

Graph DBMS
Object oriented DBMS

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

Technical documentation

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

spark.apache.org/docs/latest/sql-programming-guide.html

velocitydb.com/UserGuide

Developer

Esgyn

SADAS s.r.l.

Apache Software Foundation

VelocityDB Inc

Initial release

2015

2006

2014

2011

Current release

8.0

3.5.0 ( 2.13), September 2023

7.x

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache 2.0

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

C++, Java

C++

Scala

Server operating systems

Linux

AIX
Linux
Windows

Linux
OS X
Windows

Any that supports .NET

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC
Proprietary protocol

JDBC
ODBC

.Net

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

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

Java
Python
R
Scala

.Net

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

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

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

Based on Windows Authentication

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