DBMS > EsgynDB vs. JaguarDB vs. Oracle Rdb vs. Spark SQL vs. Yaacomo

System Properties Comparison EsgynDB vs. JaguarDB vs. Oracle Rdb vs. Spark SQL vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

Performant, highly scalable DBMS for AI and IoT applications

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Relational DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.esgyn.cn

www.jaguardb.com

www.oracle.com/database/technologies/related/rdb.html

spark.apache.org/sql

yaacomo.com

Technical documentation

www.jaguardb.com/support.html

www.oracle.com/database/technologies/related/rdb-doc.html

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

Developer

Esgyn

DataJaguar, Inc.

Oracle, originally developed by Digital Equipment Corporation (DEC)

Apache Software Foundation

Q2WEB GmbH

Initial release

2015

1984

2014

2009

Current release

3.3 July 2023

7.4.1.1, 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

commercial

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++

the server part. Clients available in other languages

Scala

Server operating systems

Linux

HP Open VMS

Linux
OS X
Windows

Android
Linux
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, 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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Multi-source replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes, on a single node

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

User concepts

Access control

fine grained access rights according to SQL-standard

rights management via user accounts

fine grained access rights 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

Oracle Adds New AI-Enabling Features To MySQL HeatWave
23 March 2023, Forbes

Should we all consolidate databases for the storage benefits? Reg vultures deploy DevOps, zoos, haircuts
18 September 2020, The Register

2013 Data Science Salary Survey – O'Reilly
4 May 2013, O'Reilly Media

provided by Google News

Use Amazon Athena with Spark SQL for your open-source transactional table formats | Amazon Web Services
24 January 2024, AWS Blog

What is Apache Spark? The big data platform that crushed Hadoop
3 April 2024, InfoWorld

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024
1 April 2024, Simplilearn

Performant IPv4 Range Spark Joins | by Jean-Claude Cote
24 January 2024, Towards Data Science

18 Top Big Data Tools and Technologies to Know About in 2024
24 January 2024, TechTarget

provided by Google News

Share this page