DBMS > Amazon DynamoDB vs. Amazon Neptune vs. GigaSpaces vs. Stardog

System Properties Comparison Amazon DynamoDB vs. Amazon Neptune vs. GigaSpaces vs. Stardog

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Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Fast, reliable graph database built for the cloud

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Document store
Key-value store

Graph DBMS
RDF store

Document store
Object oriented DBMS

Values are user defined objects

Graph DBMS
RDF store

Secondary database models

Graph DBMS
Search engine

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

Score	77.57
Rank	#16	Overall
	#2	Document stores
	#2	Key-value stores

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

Score	1.02
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	2.05
Rank	#129	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

aws.amazon.com/dynamodb

aws.amazon.com/neptune

www.gigaspaces.com

www.stardog.com

Technical documentation

docs.aws.amazon.com/dynamodb

aws.amazon.com/neptune/developer-resources

docs.gigaspaces.com/latest/landing.html

docs.stardog.com

Developer

Amazon

Gigaspaces Technologies

Stardog-Union

Initial release

2012

2017

2000

2010

Current release

15.5, September 2020

7.3.0, May 2020

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

Apache Version 2; Commercial licenses available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

Java

Server operating systems

hosted

Linux
macOS
Solaris
Windows

Linux
macOS
Windows

Data scheme

schema-free

schema-free and OWL/RDFS-schema support

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.

XML can be used for describing objects metadata

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-99 for query and DML statements

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

RESTful HTTP API

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

.Net
C++
Java
Python
Scala

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

by integration with AWS Lambda

yes, event driven architecture

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

relationships in graphs

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

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.

yes

User concepts

Access control

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

Role-based access control

Access rights for users and roles

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