DBMS > Amazon Neptune vs. EsgynDB vs. Netezza vs. ReductStore

System Properties Comparison Amazon Neptune vs. EsgynDB vs. Netezza vs. ReductStore

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Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

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

Data warehouse and analytics appliance part of IBM PureSystems

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Primary database model

Graph DBMS
RDF store

Relational DBMS

Time Series DBMS

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

Score	2.29
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Website

aws.amazon.com/neptune

www.esgyn.cn

www.ibm.com/products/netezza

github.com/reductstore
www.reduct.store

Technical documentation

aws.amazon.com/neptune/developer-resources

www.reduct.store/docs

Developer

Amazon

Esgyn

IBM

ReductStore LLC

Initial release

2017

2015

2000

2023

Current release

1.9, March 2024

License

Commercial or Open Source

commercial

Open Source

Business Source License 1.1

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++, Java

C++, Rust

Server operating systems

hosted

Linux

included in appliance

Docker
Linux
macOS
Windows

Data scheme

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

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

ADO.NET
JDBC
ODBC

JDBC
ODBC
OLE DB

HTTP API

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC/ADO.Net

C
C++
Fortran
Java
Lua
Perl
Python
R

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

Multi-source replication between multi datacenters

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

Foreign keys

Referential integrity

yes

Relationships in graphs

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

with encyption-at-rest

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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More resources

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