DBMS > Firebase Realtime Database vs. Google BigQuery vs. Hazelcast vs. Microsoft Azure Synapse Analytics vs. OrigoDB

System Properties Comparison Firebase Realtime Database vs. Google BigQuery vs. Hazelcast vs. Microsoft Azure Synapse Analytics vs. OrigoDB

Editorial information provided by DB-Engines

Name

Description

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Large scale data warehouse service with append-only tables

A widely adopted in-memory data grid

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

A fully ACID in-memory object graph database

Primary database model

Document store
Object oriented DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Website

firebase.google.com/products/realtime-database

cloud.google.com/bigquery

hazelcast.com

azure.microsoft.com/services/synapse-analytics

origodb.com

Technical documentation

firebase.google.com/docs/database

cloud.google.com/bigquery/docs

hazelcast.org/imdg/docs

docs.microsoft.com/azure/synapse-analytics

origodb.com/docs

Developer

Google

acquired by Google 2014

Google

Hazelcast

Microsoft

Robert Friberg et al

Initial release

2012

2010

2008

2016

2009

under the name LiveDB

Current release

5.3.6, November 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

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

Java

C++

Server operating systems

hosted

All OS with a Java VM

hosted

Linux
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

the object must implement a serialization strategy

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC

.NET Client API
HTTP API
LINQ

Supported programming languages

Java
JavaScript
Objective-C

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C#
Java
PHP

.Net

Server-side scripts

Stored procedures

limited functionality with using 'rules'

user defined functions

in JavaScript

yes

Event Listeners, Executor Services

Transact SQL

yes

Triggers

Callbacks are triggered when data changes

yes

Events

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Sharding, horizontal partitioning

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

depending on model

Transaction concepts

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

yes

Since BigQuery is designed for querying data

one or two-phase-commit; repeatable reads; read commited

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

In-memory capabilities

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

yes

User concepts

Access control

yes, based on authentication and database rules

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

Google Cloud Identity & Access Management (IAM)

Role-based access control

yes

Role based authorization

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