DBMS > Amazon DynamoDB vs. Datomic vs. Google BigQuery vs. JanusGraph

System Properties Comparison Amazon DynamoDB vs. Datomic vs. Google BigQuery vs. JanusGraph

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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

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

Large scale data warehouse service with append-only tables

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Primary database model

Document store
Key-value store

Relational DBMS

Graph 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	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Website

aws.amazon.com/dynamodb

www.datomic.com

cloud.google.com/bigquery

janusgraph.org

Technical documentation

docs.aws.amazon.com/dynamodb

docs.datomic.com

cloud.google.com/bigquery/docs

docs.janusgraph.org

Developer

Amazon

Cognitect

Google

Linux Foundation; originally developed as Titan by Aurelius

Initial release

2012

2010

2017

Current release

1.0.6735, June 2023

0.6.3, February 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

limited edition free

commercial

Open Source

Apache 2.0

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, Clojure

Java

Server operating systems

hosted

All OS with a Java VM

hosted

Linux
OS X
Unix
Windows

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

RESTful HTTP/JSON API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Clojure
Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Clojure
Java
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

in JavaScript

yes

Triggers

yes

by integration with AWS Lambda

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

none

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

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

Since BigQuery is designed for querying data

ACID

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

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

User concepts

Access control

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

User authentification and security via Rexster Graph Server

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