DBMS > EsgynDB vs. MonetDB vs. Newts vs. Splice Machine

System Properties Comparison EsgynDB vs. MonetDB vs. Newts vs. Splice Machine

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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

A relational database management system that stores data in columns

Time Series DBMS based on Cassandra

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

www.esgyn.cn

www.monetdb.org

opennms.github.io/newts

splicemachine.com

Technical documentation

www.monetdb.org/Documentation

github.com/OpenNMS/newts/wiki

splicemachine.com/how-it-works

Developer

Esgyn

MonetDB BV

OpenNMS Group

Splice Machine

Initial release

2015

2004

2014

Current release

Dec2023 (11.49), December 2023

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

Open Source

Apache 2.0

Open Source

AGPL 3.0, 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

C++, Java

Java

Server operating systems

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

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 2003 with some extensions

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

HTTP REST
Java API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Java

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

yes, in SQL, C, R

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding via remote tables

Sharding

based on Cassandra

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

none

Source-replica replication available in experimental status

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

In 2024 the MonetDB Foundation was established for the preservation, maintenance and further development of the ...
31 January 2024, Centrum Wiskunde & Informatica (CWI)

MonetDB Secures Investment From (and Partners With) ServiceNow
9 December 2021, Datanami

PostgreSQL, MonetDB, and Too-Big-for-Memory Data in R - Part I - DataScienceCentral.com
6 April 2018, Data Science Central

How MonetDB Exploits Modern CPU Performance | by Dwi Prasetyo Adi Nugroho
14 January 2020, Towards Data Science

Monet DB The Column-Store Pioneer - open source for you
4 September 2019, Open Source For You

provided by Google News

Machine learning data pipeline outfit Splice Machine files for insolvency
26 August 2021, The Register

Splice Machine Launches the Splice Machine Feature Store to Simplify Feature Engineering and Democratize Machine ...
19 January 2021, PR Newswire

Distributed SQL System Review: Snowflake vs Splice Machine
18 September 2019, Towards Data Science

Splice Machine Launches Feature Store to Simplify Feature Engineering
19 January 2021, Datanami

ETL: The Silent Killer of Big Data Projects
23 July 2015, insideBIGDATA

provided by Google News

Share this page