DBMS > Google BigQuery vs. Oracle NoSQL vs. Realm vs. VoltDB

System Properties Comparison Google BigQuery vs. Oracle NoSQL vs. Realm vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Document store
Key-value store
Relational DBMS

Document store

Relational DBMS

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	3.07
Rank	#86	Overall
	#15	Document stores
	#11	Key-value stores
	#47	Relational DBMS

Score	7.18
Rank	#52	Overall
	#8	Document stores

Score	1.38
Rank	#156	Overall
	#72	Relational DBMS

Website

cloud.google.com/bigquery

www.oracle.com/database/nosql/technologies/nosql

realm.io

www.voltdb.com

Technical documentation

cloud.google.com/bigquery/docs

docs.oracle.com/en/database/other-databases/nosql-database/index.html

realm.io/docs

docs.voltdb.com

Developer

Google

Oracle

Realm, acquired by MongoDB in May 2019

VoltDB Inc.

Initial release

2010

2011

2014

2010

Current release

24.1, May 2024

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Java, C++

Server operating systems

hosted

Linux
Solaris SPARC/x86

Android
Backend: server-less
iOS
Windows

Linux
OS X

for development

Data scheme

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

Typing

predefined data types such as float or date

yes

optional

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 DML and DDL statements

yes

only a subset of SQL 99

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

.Net
Java

with Android only
Objective-C
React Native
Swift

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

runs within the applications so server-side scripts are unnecessary

Java

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

Since BigQuery is designed for querying data

configurable

ACID within a storage node (=shard)

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

off heap cache

yes

In-Memory realm

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 and roles

yes

Users and roles with access to stored procedures

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