DBMS > Google BigQuery vs. Google Cloud Datastore vs. H2 vs. SiteWhere

System Properties Comparison Google BigQuery vs. Google Cloud Datastore vs. H2 vs. SiteWhere

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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

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

M2M integration platform for persisting/querying time series data

Primary database model

Secondary database models

Spatial DBMS

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Trend Chart

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	7.57
Rank	#47	Overall
	#30	Relational DBMS

Score	0.07
Rank	#347	Overall
	#33	Time Series DBMS

Website

cloud.google.com/bigquery

cloud.google.com/datastore

www.h2database.com

github.com/sitewhere/sitewhere

Technical documentation

cloud.google.com/bigquery/docs

cloud.google.com/datastore/docs

www.h2database.com/html/main.html

sitewhere1.sitewhere.io/index.html

Developer

Google

Thomas Mueller

SiteWhere

Initial release

2010

2008

2005

2010

Current release

2.2.220, July 2023

License

Commercial or Open Source

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

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

All OS with a Java VM

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

predefined scheme

Typing

predefined data types such as float or date

yes

yes, details here

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.

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

HTTP REST

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

using Google App Engine

Java Stored Procedures and User-Defined Functions

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

With clustering: 2 database servers on different computers operate on identical copies of a database

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

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

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

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (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.

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)

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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