DBMS > Firebase Realtime Database vs. Google Cloud Bigtable vs. Hive vs. MySQL vs. WakandaDB

System Properties Comparison Firebase Realtime Database vs. Google Cloud Bigtable vs. Hive vs. MySQL vs. WakandaDB

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.

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Widely used open source RDBMS

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Document store

Key-value store
Wide column store

Relational DBMS

Key/Value like access via memcached API

Object oriented DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.10
Rank	#356	Overall
	#16	Object oriented DBMS

Website

firebase.google.com/products/realtime-database

cloud.google.com/bigtable

hive.apache.org

www.mysql.com

wakanda.github.io

Technical documentation

firebase.google.com/docs/database

cloud.google.com/bigtable/docs

cwiki.apache.org/confluence/display/Hive/Home

dev.mysql.com/doc

wakanda.github.io/doc

Developer

Google

acquired by Google 2014

Google

Apache Software Foundation

initially developed by Facebook

Oracle

since 2010, originally MySQL AB, then Sun

Wakanda SAS

Initial release

2012

2015

2012

1995

2012

Current release

3.1.3, April 2022

8.4.0, April 2024

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

AGPLv3, extended commercial license available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C and C++

C++, JavaScript

Server operating systems

hosted

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

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

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

RESTful HTTP API

Supported programming languages

Java
JavaScript
Objective-C

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

C++
Java
PHP
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

JavaScript

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

yes

Triggers

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

selectable replication factor

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

yes

Atomic single-row operations

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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 rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights for users, groups and roles

Users with fine-grained authorization concept

no user groups or roles

yes

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