DBMS > Amazon DynamoDB vs. HugeGraph vs. Microsoft Azure Table Storage vs. SAP SQL Anywhere vs. Transbase

System Properties Comparison Amazon DynamoDB vs. HugeGraph vs. Microsoft Azure Table Storage vs. SAP SQL Anywhere vs. Transbase

Editorial information provided by DB-Engines

Name

Description

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

A fast-speed and highly-scalable Graph DBMS

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

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Document store
Key-value store

Graph DBMS

Wide column store

Relational DBMS

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	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

aws.amazon.com/dynamodb

github.com/hugegraph
hugegraph.apache.org

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

www.sap.com/products/technology-platform/sql-anywhere.html

www.transaction.de/en/products/transbase.html

Technical documentation

docs.aws.amazon.com/dynamodb

hugegraph.apache.org/docs

help.sap.com/docs/SAP_SQL_Anywhere

www.transaction.de/en/products/transbase/features.html

Developer

Amazon

Baidu

Microsoft

SAP

formerly Sybase

Transaction Software GmbH

Initial release

2012

2018

2012

1992

1987

Current release

0.9

17, July 2015

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache Version 2.0

commercial

free development license

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++

Server operating systems

hosted

Linux
macOS
Unix

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

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.

yes

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

RESTful HTTP API

ADO.NET
HTTP API
JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Groovy
Java
Python

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

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

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes, in C/C++, Java, .Net or Perl

yes

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

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

Source-replica replication

Database mirroring

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

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

optimistic locking

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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)

Users, roles and permissions

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

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