DBMS > Google BigQuery vs. Google Cloud Datastore vs. InterSystems IRIS vs. Newts vs. RethinkDB

System Properties Comparison Google BigQuery vs. Google Cloud Datastore vs. InterSystems IRIS vs. Newts vs. RethinkDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

A containerised multi-model DBMS, interoperability and analytics data platform with wide capabilities for vertical and horizontal scalability

Time Series DBMS based on Cassandra

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Primary database model

Relational DBMS

Document store

Document store
Key-value store
Object oriented DBMS
Relational DBMS

Time Series DBMS

Document store

Secondary database models

Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	4.05
Rank	#83	Overall
	#14	Document stores
	#10	Key-value stores
	#1	Object oriented DBMS
	#45	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Website

cloud.google.com/bigquery

cloud.google.com/datastore

www.intersystems.com/products/intersystems-iris

opennms.github.io/newts

rethinkdb.com

Technical documentation

cloud.google.com/bigquery/docs

cloud.google.com/datastore/docs

docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls

github.com/OpenNMS/newts/wiki

rethinkdb.com/docs

Developer

Google

InterSystems

OpenNMS Group

The Linux Foundation

since July 2017

Initial release

2010

2008

2018

2014

2009

Current release

2023.3, June 2023

2.4.1, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2

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

C++

Server operating systems

hosted

AIX
Linux
macOS
Ubuntu
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

depending on used data model

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

yes

string, binary, float, bool, date, geometry

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

SQL

Support of SQL

yes

SQL-like query language (GQL)

yes

APIs and other access methods

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

HTTP REST
Java API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C++
Java
JavaScript (Node.js)
Python

Java

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Server-side scripts

Stored procedures

user defined functions

in JavaScript

using Google App Engine

yes

Triggers

Callbacks using the Google Apps Engine

yes

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

range based

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Source-replica replication

selectable replication factor

based on Cassandra

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

users and table-level permissions

More information provided by the system vendor

Specific characteristics

InterSystems IRIS is a complete cloud-first data platform which includes a multi-model...
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