DBMS > Altibase vs. Amazon Redshift vs. EsgynDB vs. RDF4J vs. Vertica

System Properties Comparison Altibase vs. Amazon Redshift vs. EsgynDB vs. RDF4J vs. Vertica

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

Large scale data warehouse service for use with business intelligence tools

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

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

RDF store

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	1.07
Rank	#186	Overall
	#87	Relational DBMS

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

altibase.com

aws.amazon.com/redshift

www.esgyn.cn

rdf4j.org

www.vertica.com

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

docs.aws.amazon.com/redshift

rdf4j.org/documentation

vertica.com/documentation

Developer

Altibase

Amazon (based on PostgreSQL)

Esgyn

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

OpenText

previously Micro Focus and Hewlett Packard

Initial release

1999

2012

2015

2004

2005

Current release

v7.3, 2023, August 2023

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

C++, Java

Java

C++

Server operating systems

AIX
HP-UX
Linux

hosted

Linux

Linux
OS X
Unix
Windows

Linux

Data scheme

yes

RDF Schemas

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

restricted

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

ANSI SQL-92

yes

does not fully support an SQL-standard

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

JDBC
ODBC
Proprietary protocol

JDBC
ODBC

ADO.NET
JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

C
C++
Java

All languages supporting JDBC/ODBC

All languages supporting JDBC/ODBC/ADO.Net

Java
PHP
Python

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

Server-side scripts

Stored procedures

stored procedures and stored functions

user defined functions

in Python

Java Stored Procedures

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication between multi datacenters

none

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
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