DBMS > Amazon Redshift vs. Apache Phoenix vs. FatDB vs. Google Cloud Firestore vs. OpenTSDB

System Properties Comparison Amazon Redshift vs. Apache Phoenix vs. FatDB vs. Google Cloud Firestore vs. OpenTSDB

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

Large scale data warehouse service for use with business intelligence tools

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

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Scalable Time Series DBMS based on HBase

Primary database model

Relational DBMS

Document store
Key-value store

Document store

Time Series DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	1.68
Rank	#142	Overall
	#12	Time Series DBMS

Website

aws.amazon.com/redshift

phoenix.apache.org

firebase.google.com/products/firestore

opentsdb.net

Technical documentation

docs.aws.amazon.com/redshift

phoenix.apache.org

firebase.google.com/docs/firestore

opentsdb.net/docs/build/html/index.html

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

FatCloud

Google

currently maintained by Yahoo and other contributors

Initial release

2012

2014

2012

2017

2011

Current release

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

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

LGPL

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

Linux
Unix
Windows

Windows

hosted

Linux
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

Typing

predefined data types such as float or date

yes

numeric data for metrics, strings for tags

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

Via inetgration in SQL Server

APIs and other access methods

JDBC
ODBC

JDBC

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

HTTP API
Telnet API

Supported programming languages

All languages supporting JDBC/ODBC

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

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

Erlang
Go
Java
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

yes

via applications

yes, Firebase Rules & Cloud Functions

Triggers

yes

via applications

yes, with Cloud Functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

selectable replication factor

Multi-source replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

yes

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

fine grained access rights according to SQL-standard

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

Can implement custom security layer via applications

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

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