DBMS > Google BigQuery vs. RDF4J vs. Snowflake vs. Trino vs. VelocityDB

System Properties Comparison Google BigQuery vs. RDF4J vs. Snowflake vs. Trino vs. VelocityDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

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

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Relational DBMS

RDF store

Relational DBMS

Graph DBMS
Object oriented DBMS

Secondary database models

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	4.99
Rank	#65	Overall
	#36	Relational DBMS

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

cloud.google.com/bigquery

Technical documentation

cloud.google.com/bigquery/docs

rdf4j.org/documentation

docs.snowflake.net/manuals/index.html

trino.io/broadcast
trino.io/docs/current

velocitydb.com/UserGuide

Social network pages

Developer

Google

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Snowflake Computing Inc.

Trino Software Foundation

VelocityDB Inc

Initial release

2010

2004

2014

2012

2020 rebranded from PrestoSQL

2011

Current release

7.x

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Starburst Galaxy offers a feature-rich user interface to connect all your data sources, manage your Trino clusters, and query your data.

Implementation language

Java

Server operating systems

hosted

Linux
OS X
Unix
Windows

hosted

Linux
macOS

for devlopment

Any that supports .NET

Data scheme

yes

RDF Schemas

yes

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

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.

yes

Secondary indexes

yes

depending on connected data-source

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

CLI Client
JDBC
ODBC

JDBC
RESTful HTTP API
Trino CLI

.Net

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
PHP
Python

JavaScript (Node.js)
Python

Go
Java
JavaScript (Node.js)
Python
R
Ruby

.Net

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions

yes, depending on connected data-source

Triggers

yes

similar concept for controling cloud resources

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

none

yes

depending on connected data-source

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

depending on connected data-source

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on connected data-source

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Isolation support depends on the API used

ACID

depending on connected data-source

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

depending on connected data-source

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

Google Cloud Identity & Access Management (IAM)

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

SQL standard access control

Based on Windows Authentication

More information provided by the system vendor

Specific characteristics

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Competitive advantages

High performance analtyics and data processing of very large data sets Powerful ANSI...
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Typical application scenarios

Performant analytics query engine for data warehouses, data lakes, and data lakehouses...
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Key customers

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Market metrics

33000+ commits in GitHub 8200+ stargazers in GitHub 1200+ pull requests merged in...
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Licensing and pricing models

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