DBMS > Amazon Redshift vs. Apache Phoenix vs. Citus vs. Firebolt

System Properties Comparison Amazon Redshift vs. Apache Phoenix vs. Citus vs. Firebolt

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

Primary database model

Relational DBMS

Secondary database models

Document store

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	2.15
Rank	#117	Overall
	#56	Relational DBMS

Score	1.73
Rank	#140	Overall
	#63	Relational DBMS

Website

aws.amazon.com/redshift

phoenix.apache.org

www.citusdata.com

www.firebolt.io

Technical documentation

docs.aws.amazon.com/redshift

phoenix.apache.org

docs.citusdata.com

docs.firebolt.io

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Firebolt Analytics Inc.

Initial release

2012

2014

2010

2020

Current release

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

8.1, December 2018

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

AGPL, commercial license also available

commercial

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

Server operating systems

hosted

Linux
Unix
Windows

Linux

hosted

Data scheme

yes

late-bound, schema-on-read capabilities

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

specific XML type available, but no XML query functionality

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

standard, with numerous extensions

yes

APIs and other access methods

JDBC
ODBC

JDBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

.Net
ODBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

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

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Go
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

Source-replica replication

other methods possible by using 3rd party extensions

depending on storage layer

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

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

fine grained access rights according to SQL-standard

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