DBMS > Amazon Redshift vs. Apache Druid vs. Datastax Enterprise vs. RDFox

System Properties Comparison Amazon Redshift vs. Apache Druid vs. Datastax Enterprise vs. RDFox

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

High performance knowledge graph and semantic reasoning engine

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Wide column store

Graph DBMS
RDF store

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	5.80
Rank	#60	Overall
	#4	Wide column stores

Score	0.23
Rank	#308	Overall
	#25	Graph DBMS
	#14	RDF stores

Website

aws.amazon.com/redshift

druid.apache.org

www.datastax.com/products/datastax-enterprise

www.oxfordsemantic.tech

Technical documentation

docs.aws.amazon.com/redshift

druid.apache.org/docs/latest/design

docs.datastax.com

docs.oxfordsemantic.tech

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

DataStax

Oxford Semantic Technologies

Initial release

2012

2011

2017

Current release

29.0.1, April 2024

6.8, April 2020

6.0, Septermber 2022

License

Commercial or Open Source

commercial

Open Source

Apache license v2

commercial

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.

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Implementation language

Java

C++

Server operating systems

hosted

Linux
OS X
Unix

Linux
OS X

Linux
macOS
Windows

Data scheme

yes

schema-less columns are supported

schema-free

yes

RDF schemas

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

SQL for querying

SQL-like DML and DDL statements (CQL); Spark SQL

APIs and other access methods

JDBC
ODBC

JDBC
RESTful HTTP/JSON API

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

RESTful HTTP API
SPARQL 1.1

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

C
Java

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

configurable replication factor, datacenter aware, advanced replication for edge computing

replication via a shared file system

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

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

Atomicity and isolation are supported for single operations

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights for users can be defined per object

Roles, resources, and access types

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
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Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
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Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
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Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
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Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
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Licensing and pricing models

Annual subscription
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