DBMS > Amazon Redshift vs. Apache Cassandra vs. Quasardb vs. Riak TS

System Properties Comparison Amazon Redshift vs. Apache Cassandra vs. Quasardb vs. Riak TS

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

Distributed, high-performance timeseries database

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Relational DBMS

Wide column store

Time Series DBMS

Secondary database models

Vector DBMS

starting with release V5

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	98.94
Rank	#11	Overall
	#1	Wide column stores

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Website

aws.amazon.com/redshift

cassandra.apache.org

quasar.ai

Technical documentation

docs.aws.amazon.com/redshift

cassandra.apache.org/doc/latest

doc.quasar.ai/master

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top level project, originally developped by Facebook

quasardb

Open Source, formerly Basho Technologies

Initial release

2012

2008

2009

2015

Current release

5.0-rc1, July 2024

3.14.1, January 2024

3.0.0, September 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

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

Open Source

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.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

C++

Erlang

Server operating systems

hosted

BSD
Linux
OS X
Windows

Linux
OS X

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

only equality queries, not always the best performing solution

yes

with tags

restricted

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like SELECT, DML and DDL statements (CQL)

SQL-like query language

yes, limited

APIs and other access methods

JDBC
ODBC

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in Python

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

consistent hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Representation of geographical distribution of servers is possible

Source-replica replication with selectable replication factor

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

links between datasets can be stored

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

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

Access rights for users can be defined per object

Cryptographically strong user authentication and audit trail

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
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Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
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Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
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Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
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Market metrics

Apache Cassandra is used by 40% of the Fortune 100.
» more

Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
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