DBMS > EXASOL vs. Google Cloud Firestore vs. Neo4j vs. Snowflake vs. TerarkDB

System Properties Comparison EXASOL vs. Google Cloud Firestore vs. Neo4j vs. Snowflake vs. TerarkDB

Editorial information provided by DB-Engines

Name

Description

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

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Open source graph database

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

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

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

Score	3.24
Rank	#91	Overall
	#47	Relational DBMS

Score	7.45
Rank	#54	Overall
	#9	Document stores

Score	52.32
Rank	#18	Overall
	#1	Graph DBMS

Score	23.19
Rank	#30	Overall
	#17	Relational DBMS

Score	0.04
Rank	#319	Overall
	#50	Key-value stores

Website

www.exasol.com

firebase.google.com/products/firestore

neo4j.com

www.snowflake.com

github.com/Terark/terarkdb

Technical documentation

www.exasol.com/resources

firebase.google.com/docs/firestore

neo4j.com/docs

docs.snowflake.net/manuals/index.html

github.com/Terark/terarkdb/wiki

Developer

Exasol

Google

Neo4j, Inc.

Snowflake Computing Inc.

Terark

Initial release

2000

2017

2007

2014

2016

Current release

4.2.3, January 2021

License

Commercial or Open Source

commercial

Open Source

GPL version3, commercial licenses available

commercial

restricted open source version available

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, Scala

C++

Server operating systems

hosted

Linux

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

hosted

Data scheme

yes

schema-free

schema-free and schema-optional

yes

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

schema-free

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

yes

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

Bolt protocol
Cypher query language
Java API
Neo4j-OGM

Object Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3

CLI Client
JDBC
ODBC

C++ API
Java API

Supported programming languages

Java
Lua
Python
R

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

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

JavaScript (Node.js)
Python

C++
Java

Server-side scripts

Stored procedures

user defined functions

yes, Firebase Rules & Cloud Functions

yes

User defined Procedures and Functions

user defined functions

Triggers

yes

yes, with Cloud Functions

yes

via event handler

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Causal Clustering using Raft protocol

available in in Enterprise Version only

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Using Cloud Dataflow

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

Relationships in graphs

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

yes

ACID

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, groups and roles according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

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

Neo4j is a native graph database platform that is built to store, query, analyze...
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Competitive advantages

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Typical application scenarios

Real-Time Recommendations Master Data Management Identity and Access Management Network...
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Key customers

Over 300 commercial customers and over 750 startups use Neo4j. Flagship customers...
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Market metrics

Neo4j boasts the world's largest graph database ecosystem with more than a 15 million...
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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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News

This Week in Neo4j – Information Extraction Pipeline, Chakra UI Component Library, NODES 2021
27 February 2021

Graphs in China: 5-Minute Interview with Richard Wu & George Zhang
26 February 2021

Unearthing Historical Networks Using Neo4j and Archival Documents
25 February 2021

Top 10 Use Cases: Empowering Network and IT Operations Management
22 February 2021

This Week in Neo4j – Structured data embedded in web pages, Speaker Listener LPA, Neo4j at NASA
20 February 2021

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.