DBMS > Amazon Aurora vs. Google Cloud Datastore vs. Microsoft Azure Table Storage vs. SAP HANA vs. TerminusDB

System Properties Comparison Amazon Aurora vs. Google Cloud Datastore vs. Microsoft Azure Table Storage vs. SAP HANA vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

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

A Wide Column Store for rapid development using massive semi-structured datasets

In-memory, column based data store. Available as appliance or cloud service

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Secondary database models

Document store

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Document store
RDF store

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

Score	7.91
Rank	#50	Overall
	#32	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

aws.amazon.com/rds/aurora

cloud.google.com/datastore

azure.microsoft.com/en-us/services/storage/tables

www.sap.com/products/hana.html

terminusdb.com

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

cloud.google.com/datastore/docs

help.sap.com/hana

terminusdb.github.io/terminusdb/#

Developer

Amazon

Google

Microsoft

SAP

DataChemist Ltd.

Initial release

2015

2008

2012

2010

2018

Current release

2.0 SPS07 (April 4, 2023), April 2023

11.0.0, January 2023

License

Commercial or Open Source

commercial

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Prolog, Rust

Server operating systems

hosted

Appliance or cloud-service

Linux

Data scheme

yes

schema-free

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language (GQL)

yes

SQL-like query language (WOQL)

APIs and other access methods

ADO.NET
JDBC
ODBC

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

RESTful HTTP API

JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript
Python

Server-side scripts

Stored procedures

yes

using Google App Engine

SQLScript, R

yes

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Implicit feature of the cloud service

yes

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication using Paxos

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Serializable Isolation within Transactions, Read Committed outside of Transactions

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory journaling

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

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

Access rights based on private key authentication or shared access signatures

yes

Role-based access control

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