DBMS > Firebase Realtime Database vs. Microsoft Azure Table Storage vs. MySQL vs. Teradata Aster vs. Warp 10

System Properties Comparison Firebase Realtime Database vs. Microsoft Azure Table Storage vs. MySQL vs. Teradata Aster vs. Warp 10

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

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

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

Widely used open source RDBMS

Platform for big data analytics on multistructured data sources and types

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Document store

Wide column store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

firebase.google.com/products/realtime-database

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

www.mysql.com

www.warp10.io

Technical documentation

firebase.google.com/docs/database

dev.mysql.com/doc

www.warp10.io/content/02_Getting_started

Developer

Google

acquired by Google 2014

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Teradata

SenX

Initial release

2012

1995

2005

2015

Current release

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Open Source

Apache License 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C and C++

Java

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

schema-free

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
ODBC
OLE DB

HTTP API
Jupyter
WebSocket

Supported programming languages

Java
JavaScript
Objective-C

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

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

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes

proprietary syntax

R packages

yes

WarpScript

Triggers

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Multi-source replication
Source-replica replication

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

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 depending on configuration

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

yes

optimistic locking

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 based on private key authentication or shared access signatures

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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