DBMS > Apache Phoenix vs. Firebase Realtime Database vs. Google BigQuery vs. Quasardb vs. Yanza

System Properties Comparison Apache Phoenix vs. Firebase Realtime Database vs. Google BigQuery vs. Quasardb vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

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

Distributed, high-performance timeseries database

Time Series DBMS for IoT Applications

Primary database model

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

phoenix.apache.org

firebase.google.com/products/realtime-database

cloud.google.com/bigquery

quasar.ai

yanza.com

Technical documentation

phoenix.apache.org

firebase.google.com/docs/database

cloud.google.com/bigquery/docs

doc.quasar.ai/master

Developer

Apache Software Foundation

Google

acquired by Google 2014

Google

quasardb

Yanza

Initial release

2014

2012

2010

2009

2015

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

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

Linux
Unix
Windows

hosted

BSD
Linux
OS X
Windows

Windows

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

JDBC

Android
iOS
JavaScript API
RESTful HTTP API

RESTful HTTP/JSON API

HTTP API

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

Java
JavaScript
Objective-C

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

any language that supports HTTP calls

Server-side scripts

Stored procedures

user defined functions

limited functionality with using 'rules'

user defined functions

in JavaScript

Triggers

Callbacks are triggered when data changes

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication with selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

yes

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

yes, based on authentication and database rules

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

Google Cloud Identity & Access Management (IAM)

Cryptographically strong user authentication and audit trail

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