DBMS > Amazon DynamoDB vs. Google Cloud Datastore vs. RDF4J vs. SAP Adaptive Server vs. SwayDB

System Properties Comparison Amazon DynamoDB vs. Google Cloud Datastore vs. RDF4J vs. SAP Adaptive Server vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

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

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Document store
Key-value store

Secondary database models

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	35.09
Rank	#25	Overall
	#17	Relational DBMS

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Website

aws.amazon.com/dynamodb

cloud.google.com/datastore

rdf4j.org

www.sap.com/products/technology-platform/sybase-ase.html

swaydb.simer.au

Technical documentation

docs.aws.amazon.com/dynamodb

cloud.google.com/datastore/docs

rdf4j.org/documentation

help.sap.com/docs/SAP_ASE

Developer

Amazon

Google

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

SAP, Sybase

Simer Plaha

Initial release

2012

2008

2004

1987

2018

Current release

16.0

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

Open Source

GNU Affero GPL V3.0

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

C and C++

Scala

Server operating systems

hosted

Linux
OS X
Unix
Windows

AIX
HP-UX
Linux
Solaris
Unix
Windows

Data scheme

schema-free

yes

RDF Schemas

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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 query language (GQL)

yes

APIs and other access methods

RESTful HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
PHP
Python

C
C++
Cobol
Java
Java
Perl
PHP
Python

Java
Kotlin
Scala

Server-side scripts

Stored procedures

using Google App Engine

yes

Java and Transact-SQL

Triggers

yes

by integration with AWS Lambda

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

none

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

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

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Isolation support depends on the API used

ACID

Atomic execution of operations

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

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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