DBMS > Amazon Redshift vs. FatDB vs. InfluxDB vs. OushuDB vs. Spark SQL

System Properties Comparison Amazon Redshift vs. FatDB vs. InfluxDB vs. OushuDB vs. Spark SQL

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

DBMS for storing time series, events and metrics

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Document store
Key-value store

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

with GEO package

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	0.04
Rank	#363	Overall
	#154	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/redshift

www.influxdata.com/products/influxdb-overview

www.oushu.com/product/oushuDB

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/redshift

docs.influxdata.com/influxdb

www.oushu.com/documentation

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Amazon (based on PostgreSQL)

FatCloud

Oushu

Apache Software Foundation

Initial release

2012

2013

2014

Current release

2.7.6, April 2024

4.0.1, August 2020

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

MIT-License; commercial enterprise version available

commercial

Open Source

Apache 2.0

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

Scala

Server operating systems

hosted

Windows

Linux
OS X

through Homebrew

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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

Via inetgration in SQL Server

SQL-like query language

Full-featured ANSI SQL support

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

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

HTTP API
JSON over UDP

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

C
C++

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

via applications

yes

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

in enterprise version only

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

in enterprise version only

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

Depending on used storage engine

User concepts

Access control

fine grained access rights according to SQL-standard

Can implement custom security layer via applications

simple rights management via user accounts

Kerberos, SSL and role based access

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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