DBMS > Amazon Redshift vs. DataFS vs. Quasardb vs. Spark SQL

System Properties Comparison Amazon Redshift vs. DataFS vs. Quasardb vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Distributed, high-performance timeseries database

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

Primary database model

Secondary database models

Graph DBMS

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Trend Chart

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/redshift

newdatabase.com

quasar.ai

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/redshift

dev.mobiland.com/Overview.xsp

doc.quasar.ai/master

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

Developer

Amazon (based on PostgreSQL)

Mobiland AG

quasardb

Apache Software Foundation

Initial release

2012

2018

2009

2014

Current release

1.1.263, October 2022

3.14.1, January 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

C++

Scala

Server operating systems

hosted

Windows

BSD
Linux
OS X
Windows

Linux
OS X
Windows

Data scheme

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

.NET Client API
Proprietary client DLL
WinRT client

HTTP API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C#
C++
VB.Net

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Sharding

Proprietary Sharding system

Sharding

consistent hashing

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication with selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

by using LevelDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

Windows-Profile

Cryptographically strong user authentication and audit trail

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