DBMS > Amazon Redshift vs. Apache Impala vs. FatDB vs. Hypertable

System Properties Comparison Amazon Redshift vs. Apache Impala vs. FatDB vs. Hypertable

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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.

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

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

An open source BigTable implementation based on distributed file systems such as Hadoop

Primary database model

Relational DBMS

Document store
Key-value store

Wide column store

Secondary database models

Document store

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Website

aws.amazon.com/redshift

impala.apache.org

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

FatCloud

Hypertable Inc.

Initial release

2012

2013

2012

2009

Current release

4.1.0, June 2022

0.9.8.11, March 2016

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

GNU version 3. Commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Server operating systems

hosted

Linux

Windows

Linux
OS X
Windows

an inofficial Windows port is available

Data scheme

yes

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.

Secondary indexes

restricted

yes

restricted

only exact value or prefix value scans

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

Via inetgration in SQL Server

APIs and other access methods

JDBC
ODBC

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

C++ API
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

yes

via applications

Triggers

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

selectable replication factor on file system level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

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

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 rights for users, groups and roles

based on Apache Sentry and Kerberos

Can implement custom security layer via applications

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