DBMS > Google BigQuery vs. Google Cloud Bigtable vs. Google Cloud Firestore vs. Ingres vs. SiteWhere

System Properties Comparison Google BigQuery vs. Google Cloud Bigtable vs. Google Cloud Firestore vs. Ingres vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

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.

Well established RDBMS

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

Key-value store
Wide column store

Document store

Relational DBMS

Time Series DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	4.11
Rank	#81	Overall
	#44	Relational DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

cloud.google.com/bigquery

cloud.google.com/bigtable

firebase.google.com/products/firestore

www.actian.com/databases/ingres

github.com/sitewhere/sitewhere

Technical documentation

cloud.google.com/bigquery/docs

cloud.google.com/bigtable/docs

firebase.google.com/docs/firestore

docs.actian.com/ingres

sitewhere1.sitewhere.io/index.html

Developer

Google

Actian Corporation

SiteWhere

Initial release

2010

2015

2017

1974

originally developed at University Berkely in early 1970s

2010

Current release

11.2, May 2022

License

Commercial or Open Source

commercial

Open Source

Common Public Attribution License Version 1.0

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

Server operating systems

hosted

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

predefined scheme

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.

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

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

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

HTTP REST

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, Firebase Rules & Cloud Functions

yes

Triggers

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

Multi-source replication

Ingres Replicator

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

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

Atomic single-row operations

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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 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)

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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