DBMS > Amazon Redshift vs. Apache Druid vs. TimescaleDB vs. Virtuoso

System Properties Comparison Amazon Redshift vs. Apache Druid vs. TimescaleDB vs. Virtuoso

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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

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Virtuoso is a multi-model hybrid-RDBMS that supports management of data represented as relational tables and/or property graphs

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Time Series DBMS

Document store
Graph DBMS
Native XML DBMS
Relational DBMS
RDF store
Search engine

Secondary database models

Relational DBMS

Spatial 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	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	4.26
Rank	#78	Overall
	#13	Document stores
	#4	Graph DBMS
	#2	Native XML DBMS
	#42	Relational DBMS
	#2	RDF stores
	#9	Search engines

Website

aws.amazon.com/redshift

druid.apache.org

www.timescale.com

virtuoso.openlinksw.com

Technical documentation

docs.aws.amazon.com/redshift

druid.apache.org/docs/latest/design

docs.timescale.com

docs.openlinksw.com/virtuoso

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

Timescale

OpenLink Software

Initial release

2012

2017

1998

Current release

29.0.1, April 2024

2.13.0, November 2023

7.2.11, September 2023

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

Apache 2.0

Open Source

GPLv2, extended commercial license available

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

Java

Server operating systems

hosted

Linux
OS X
Unix

Linux
OS X
Windows

AIX
FreeBSD
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-less columns are supported

yes

SQL - Standard relational schema
RDF - Quad (S, P, O, G) or Triple (S, P, O)
XML - DTD, XML Schema
DAV - freeform filesystem objects, plus User Defined Types a/k/a Dynamic Extension Type

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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.

yes

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL for querying

yes

full PostgreSQL SQL syntax

yes

SQL-92, SQL-200x, SQL-3, SQLX

APIs and other access methods

JDBC
ODBC

JDBC
RESTful HTTP/JSON API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

ADO.NET
GeoSPARQL
HTTP API
JDBC
Jena RDF API
ODBC
OLE DB
RDF4J API
RESTful HTTP API
Sesame REST HTTP Protocol
SOAP webservices
SPARQL 1.1
WebDAV
XPath
XQuery
XSLT

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

.Net
C
C#
C++
Java
JavaScript
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

yes

Virtuoso PL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

yes, across time and space (hash partitioning) attributes

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

Source-replica replication with hot standby and reads on replicas

Chain, star, and bi-directional replication
Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

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

fine grained access rights according to SQL-standard

Fine-grained Attribute-Based Access Control (ABAC) in addition to typical coarse-grained Role-Based Access Control (RBAC) according to SQL-standard. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

Used for — Analytics/BI Conceptual Data Virtualization Enterprise Knowledge Graphs...
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Key customers

Broad use across enterprises and governments including — European Union (EU) US Government...
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Market metrics

Largest installed-base of Multi-Model RDBMS for AI-friendly Knowledge Graphs Platform...
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Licensing and pricing models

Available in both Commercial Enterprise and Open Source (GPL v2) Editions Feature...
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