DBMS > Amazon Redshift vs. Apache Druid vs. atoti vs. EXASOL

System Properties Comparison Amazon Redshift vs. Apache Druid vs. atoti vs. EXASOL

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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

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Object oriented DBMS

Relational DBMS

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	0.59
Rank	#242	Overall
	#10	Object oriented DBMS

Score	2.25
Rank	#120	Overall
	#57	Relational DBMS

Website

aws.amazon.com/redshift

druid.apache.org

atoti.io

www.exasol.com

Technical documentation

docs.aws.amazon.com/redshift

druid.apache.org/docs/latest/design

docs.atoti.io

www.exasol.com/resources

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

ActiveViam

Exasol

Initial release

2012

2000

Current release

29.0.1, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache license v2

commercial

free versions available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

Linux
OS X
Unix

Data scheme

yes

schema-less columns are supported

yes

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

Multidimensional Expressions (MDX)

yes

APIs and other access methods

JDBC
ODBC

JDBC
RESTful HTTP/JSON API

.Net
JDBC
ODBC
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Java
Lua
Python
R

Server-side scripts

Stored procedures

user defined functions

in Python

Python

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

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

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

yes, multi-version concurrency control (MVCC)

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, groups and roles according to SQL-standard

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