DBMS > Google BigQuery vs. Microsoft Azure SQL Database vs. Newts vs. RDF4J vs. Riak TS

System Properties Comparison Google BigQuery vs. Microsoft Azure SQL Database vs. Newts vs. RDF4J vs. Riak TS

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Database as a Service offering with high compatibility to Microsoft SQL Server

Time Series DBMS based on Cassandra

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

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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Trend Chart

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	76.78
Rank	#16	Overall
	#11	Relational DBMS

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Website

cloud.google.com/bigquery

azure.microsoft.com/en-us/products/azure-sql/database

opennms.github.io/newts

rdf4j.org

Technical documentation

cloud.google.com/bigquery/docs

docs.microsoft.com/en-us/azure/azure-sql

github.com/OpenNMS/newts/wiki

rdf4j.org/documentation

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Google

Microsoft

OpenNMS Group

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

Open Source, formerly Basho Technologies

Initial release

2010

2014

2004

2015

Current release

V12

3.0.0, September 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

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

C++

Java

Erlang

Server operating systems

hosted

Linux
OS X
Windows

Linux
OS X
Unix
Windows

Linux
OS X

Data scheme

yes

schema-free

yes

RDF Schemas

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

restricted

SQL

Support of SQL

yes

yes, limited

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

HTTP REST
Java API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

HTTP API
Native Erlang Interface

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Java

Java
PHP
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Transact SQL

yes

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with always 3 replicas available

selectable replication factor

based on Cassandra

none

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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