DBMS > Amazon Neptune vs. Newts vs. Oracle NoSQL vs. TimesTen

System Properties Comparison Amazon Neptune vs. Newts vs. Oracle NoSQL vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Time Series DBMS based on Cassandra

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

In-Memory RDBMS compatible to Oracle

Primary database model

Graph DBMS
RDF store

Time Series DBMS

Document store
Key-value store
Relational DBMS

Relational DBMS

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

Score	2.58
Rank	#112	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	2.96
Rank	#103	Overall
	#18	Document stores
	#17	Key-value stores
	#52	Relational DBMS

Score	1.35
Rank	#165	Overall
	#75	Relational DBMS

Website

aws.amazon.com/neptune

opennms.github.io/newts

www.oracle.com/database/nosql/technologies/nosql

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

Technical documentation

aws.amazon.com/neptune/developer-resources

github.com/OpenNMS/newts/wiki

docs.oracle.com/en/database/other-databases/nosql-database/index.html

docs.oracle.com/database/timesten-18.1

Developer

Amazon

OpenNMS Group

Oracle

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2017

2014

2011

1998

Current release

23.3, December 2023

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

commercial

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

Java

Server operating systems

hosted

Linux
OS X
Windows

Linux
Solaris SPARC/x86

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

Typing

predefined data types such as float or date

yes

optional

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

SQL-like DML and DDL statements

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

HTTP REST
Java API

RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

Java

C
C#
Go
Java
JavaScript (Node.js)
Python

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

PL/SQL

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

none

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.

selectable replication factor

based on Cassandra

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

off heap cache

yes

User concepts

Access control

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

Access rights for users and roles

fine grained access rights according to SQL-standard

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