DBMS > Firebase Realtime Database vs. HBase vs. Microsoft Azure Table Storage vs. Riak KV

System Properties Comparison Firebase Realtime Database vs. HBase vs. Microsoft Azure Table Storage vs. Riak KV

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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.

Wide-column store based on Apache Hadoop and on concepts of BigTable

A Wide Column Store for rapid development using massive semi-structured datasets

Distributed, fault tolerant key-value store

Primary database model

Document store

Wide column store

Key-value store

with links between data sets and object tags for the creation of secondary indexes

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

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

firebase.google.com/products/realtime-database

hbase.apache.org

azure.microsoft.com/en-us/services/storage/tables

Technical documentation

firebase.google.com/docs/database

hbase.apache.org/book.html

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Google

acquired by Google 2014

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

OpenSource, formerly Basho Technologies

Initial release

2012

2008

2012

2009

Current release

2.3.4, January 2021

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

Open Source

Apache version 2, commercial enterprise edition

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

Erlang

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

hosted

Linux
OS X

Data scheme

schema-free

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

restricted

SQL

Support of SQL

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

Java
JavaScript
Objective-C

C
C#
C++
Groovy
Java
PHP
Python
Scala

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes

Coprocessors in Java

Erlang

Triggers

Callbacks are triggered when data changes

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

selectable replication factor

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 or Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

Transaction concepts

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

yes

Single row ACID (across millions of columns)

optimistic locking

Concurrency

Support for concurrent manipulation of data

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

yes, based on authentication and database rules

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Access rights based on private key authentication or shared access signatures

yes, using Riak Security

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More resources

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