DBMS > CouchDB vs. GraphDB vs. Snowflake vs. Vertica

System Properties Comparison CouchDB vs. GraphDB vs. Snowflake vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Enterprise-ready RDF and graph database with efficient reasoning, cluster and external index synchronization support. It supports also SQL JDBC access to Knowledge Graph and GraphQL over SPARQL.

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

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Document store

Graph DBMS
RDF store

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS

using the Geocouch extension

Spatial DBMS
Time Series DBMS

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

Score	7.46
Rank	#51	Overall
	#7	Document stores

Score	2.76
Rank	#99	Overall
	#8	Graph DBMS
	#4	RDF stores

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

Technical documentation

docs.couchdb.org/en/stable

graphdb.ontotext.com/documentation

docs.snowflake.net/manuals/index.html

vertica.com/documentation

Social network pages

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Ontotext

Snowflake Computing Inc.

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2005

2000

2014

2005

Current release

3.3.3, December 2023

10.4, October 2023

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Some plugins of GraphDB Workbench are open sourced

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Erlang

Java

C++

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM
Linux
OS X
Windows

hosted

Linux

Data scheme

schema-free

schema-free and OWL/RDFS-schema support; RDF shapes

yes

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

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

via views

yes, supports real-time synchronization and indexing in SOLR/Elastic search/Lucene and GeoSPARQL geometry data indexes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

stored SPARQL accessed as SQL using Apache Calcite through JDBC/ODBC

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

RESTful HTTP/JSON API

GeoSPARQL
GraphQL
GraphQL Federation
Java API
JDBC
RDF4J API
RDFS
RIO
Sail API
Sesame REST HTTP Protocol
SPARQL 1.1

CLI Client
JDBC
ODBC

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

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

JavaScript (Node.js)
Python

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

View functions in JavaScript

well-defined plugin interfaces; JavaScript server-side extensibility

user defined functions

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

similar concept for controling cloud resources

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

none

yes

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency, Eventual consistency (configurable in cluster mode per master or individual client request)

Immediate Consistency

Foreign keys

Referential integrity

yes

Constraint checking

yes

Transaction concepts

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

atomic operations within a single document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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.

User concepts

Access control

Access rights for users can be defined per database

Default Basic authentication through RDF4J client, or via Java when run with cURL, default token-based in the Workbench or via Rest API, optional access through OpenID or Kerberos single sign-on.

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

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Ontotext GraphDB is a semantic database engine that allows organizations to build...
» more

Competitive advantages

GraphDB allows you to link text and data in big knowledge graphs. It’s easy to experiment...
» more

Typical application scenarios

Metadata enrichment and management, linked data publishing, semantic inferencing...
» more

Key customers

GraphDB provides a platform for building next-generation AI and Knowledge Graph...
» more

Market metrics

GraphDB is the most utilized semantic triplestore for mission-critical enterprise...
» more

Licensing and pricing models

GraphDB Free is a non-commercial version and is free to use. GraphDB Enterprise edition...
» more

News

GraphDB in Action: Using Semantics To Push The Envelope Of Software Engineering, Machine Learning, and E-Health Domains
11 September 2024

Accelerating End-to-end Knowledge Graph Solutions with Ontotext’s LinkedLifeData Inventory: The Case of Target Discovery
30 August 2024

Using Entity Linking to Turn Your Graph into a Knowledge Graph
23 August 2024

Considerations to Creating a Graph Center of Excellence: 5 Elements to Ensure Success
22 August 2024

The Power of Knowledge Graphs – A Chat with Perfect Memory and Ontotext
16 August 2024

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