DBMS > ArcadeDB vs. Datomic vs. EXASOL vs. Neo4j vs. Snowflake

System Properties Comparison ArcadeDB vs. Datomic vs. EXASOL vs. Neo4j vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

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

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Scalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offerings

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Relational DBMS

Graph DBMS

Relational DBMS

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

Score	0.10
Rank	#358	Overall
	#48	Document stores
	#38	Graph DBMS
	#52	Key-value stores
	#35	Time Series DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	44.89
Rank	#21	Overall
	#1	Graph DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

Technical documentation

docs.arcadedb.com

docs.datomic.com

www.exasol.com/resources

neo4j.com/docs

docs.snowflake.net/manuals/index.html

Developer

Arcade Data

Cognitect

Exasol

Neo4j, Inc.

Snowflake Computing Inc.

Initial release

2021

2012

2000

2007

2014

Current release

September 2021

1.0.6735, June 2023

5.20, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

limited edition free

commercial

Open Source

GPL version3, commercial licenses available

commercial

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.

Neo4j Aura: Neo4j’s fully managed cloud service: The zero-admin, always-on graph database for cloud developers.

Implementation language

Java

Java, Clojure

Java, Scala

Server operating systems

All OS with a Java VM

Linux

Can also be used server-less as embedded Java database.
OS X
Solaris
Windows

hosted

Data scheme

schema-free

yes

schema-free and schema-optional

yes

support of semi-structured data formats (JSON, XML, Avro)

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

pluggable indexing subsystem, by default Apache Lucene

SQL

Support of SQL

SQL-like query language, no joins

yes

APIs and other access methods

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

Bolt protocol
Cypher query language
Java API
Neo4j-OGM

Object Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3

CLI Client
JDBC
ODBC

Supported programming languages

Java

Clojure
Java

Java
Lua
Python
R

.Net
Clojure
Elixir
Go
Groovy
Haskell
Java
JavaScript
Perl
PHP
Python
Ruby
Scala

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

yes

User defined Procedures and Functions

user defined functions

Triggers

By using transaction functions

yes

via event handler

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

yes using Neo4j Fabric

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

But extensive use of caching in the application peers

Causal Clustering using Raft protocol

available in in Enterprise Version only

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Causal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Relationships in graphs

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of 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

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

Access rights for users, groups and roles according to SQL-standard

Users, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)

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

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

GPL v3 license that can be used all the places where you might use MySQL. Neo4j Commercial...
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