DBMS > AllegroGraph vs. Amazon DynamoDB vs. Datomic vs. HBase

System Properties Comparison AllegroGraph vs. Amazon DynamoDB vs. Datomic vs. HBase

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Editorial information provided by DB-Engines

Name

Description

High performance, persistent RDF store with additional support for Graph DBMS

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

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Wide-column store based on Apache Hadoop and on concepts of BigTable

Primary database model

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Document store
Key-value store

Relational DBMS

Wide column store

Secondary database models

Spatial DBMS

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

Score	1.06
Rank	#187	Overall
	#30	Document stores
	#17	Graph DBMS
	#7	RDF stores
	#7	Vector DBMS

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Website

allegrograph.com

aws.amazon.com/dynamodb

www.datomic.com

hbase.apache.org

Technical documentation

franz.com/agraph/support/documentation/current/agraph-introduction.html

docs.aws.amazon.com/dynamodb

docs.datomic.com

hbase.apache.org/book.html

Developer

Franz Inc.

Amazon

Cognitect

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Initial release

2004

2012

2008

Current release

8.0, December 2023

1.0.6735, June 2023

2.3.4, January 2021

License

Commercial or Open Source

commercial

Limited community edition free

commercial

free tier for a limited amount of database operations

commercial

limited edition free

Open Source

Apache version 2

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, Clojure

Java

Server operating systems

Linux
OS X
Windows

hosted

All OS with a Java VM

Linux
Unix
Windows

using Cygwin

Data scheme

yes

RDF schemas

schema-free

yes

schema-free, schema definition possible

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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.

bulk load of XML files possible

Secondary indexes

yes

SQL

Support of SQL

SPARQL is used as query language

APIs and other access methods

RESTful HTTP API
SPARQL

RESTful HTTP API

Java API
RESTful HTTP API
Thrift

Supported programming languages

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Clojure
Java

C
C#
C++
Groovy
Java
PHP
Python
Scala

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

yes

Transaction Functions

yes

Coprocessors in Java

Triggers

yes

by integration with AWS Lambda

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

with Federation

Sharding

none

But extensive use of caching in the application peers

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

none

But extensive use of caching in the application peers

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

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

Single row ACID (across millions of columns)

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

Users with fine-grained authorization concept, user roles and pluggable authentication

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

More information provided by the system vendor

Specific characteristics

Knowledge Graph Platform Leader FedShard - Designed for Entity-Event Knowledge Graph...
» more

Competitive advantages

AllegroGraph is uniquely suited to support adhoc queries through SPARQL, Prolog and...
» more

News

Can Neuro-Symbolic AI Solve AI’s Weaknesses?
17 April 2024

100 Companies That Matter in KM – Franz Inc.
3 April 2024

Exploring AllegroGraph v8 – Unleashing the Power of Neuro-Symbolic AI (Recorded Webinar)
9 February 2024

What is Neuro-Symbolic AI?
23 January 2024

Allegro CL v11 – Now Available! – The Neuro-Symbolic AI Programming Platform
8 January 2024

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