DBMS > EsgynDB vs. Google BigQuery vs. JaguarDB vs. MonetDB vs. OrientDB

System Properties Comparison EsgynDB vs. Google BigQuery vs. JaguarDB vs. MonetDB vs. OrientDB

Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

Performant, highly scalable DBMS for AI and IoT applications

A relational database management system that stores data in columns

Multi-model DBMS (Document, Graph, Key/Value)

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store

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	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#13	Vector DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	3.25
Rank	#89	Overall
	#16	Document stores
	#6	Graph DBMS
	#13	Key-value stores

Website

www.esgyn.cn

cloud.google.com/bigquery

www.jaguardb.com

www.monetdb.org

orientdb.org

Technical documentation

cloud.google.com/bigquery/docs

www.jaguardb.com/support.html

www.monetdb.org/Documentation

www.orientdb.com/docs/last/index.html

Developer

Esgyn

Google

DataJaguar, Inc.

MonetDB BV

OrientDB LTD; CallidusCloud; SAP

Initial release

2015

2010

2015

2004

2010

Current release

3.3 July 2023

Dec2023 (11.49), December 2023

3.2.29, March 2024

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

Open Source

Mozilla Public License 2.0

Open Source

Apache version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Java

C++

the server part. Clients available in other languages

Java

Server operating systems

Linux

hosted

Linux

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java JDK (>= JDK 6)

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

SQL 2003 with some extensions

SQL-like query language, no joins

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP/JSON API

JDBC
ODBC

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

in JavaScript

yes, in SQL, C, R

Java, Javascript

Triggers

yes

Hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Sharding via remote tables

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Multi-source replication

none

Source-replica replication available in experimental status

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

ACID

Since BigQuery is designed for querying 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.

User concepts

Access control

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

rights management via user accounts

fine grained access rights according to SQL-standard

Access rights for users and roles; record level security configurable

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