DBMS > Amazon Redshift vs. Apache Phoenix vs. Fauna vs. Hive vs. JaguarDB

System Properties Comparison Amazon Redshift vs. Apache Phoenix vs. Fauna vs. Hive vs. JaguarDB

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

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

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

Performant, highly scalable DBMS for AI and IoT applications

Primary database model

Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store
Vector DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	1.52
Rank	#153	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

phoenix.apache.org

docs.fauna.com

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

www.jaguardb.com/support.html

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Fauna, Inc.

Apache Software Foundation

initially developed by Facebook

DataJaguar, Inc.

Initial release

2012

2014

2012

2015

Current release

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

3.1.3, April 2022

3.3 July 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2

Open Source

GPL V3.0

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

Scala

Java

C++

the server part. Clients available in other languages

Server operating systems

hosted

Linux
Unix
Windows

hosted

All OS with a Java VM

Linux

Data scheme

yes

late-bound, schema-on-read capabilities

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like DML and DDL statements

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

JDBC
ODBC

JDBC

RESTful HTTP API

JDBC
ODBC
Thrift

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

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

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

C++
Java
PHP
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

consistent hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

Multi-source replication

selectable replication factor

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual 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

Identity management, authentication, and access control

Access rights for users, groups and roles

rights management via user accounts

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