DBMS > Amazon Redshift vs. Coveo vs. Datomic vs. EventStoreDB

System Properties Comparison Amazon Redshift vs. Coveo vs. Datomic vs. EventStoreDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Primary database model

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	2.14
Rank	#116	Overall
	#11	Search engines

Score	1.55
Rank	#144	Overall
	#67	Relational DBMS

Score	1.07
Rank	#181	Overall
	#1	Event Stores

Website

aws.amazon.com/redshift

www.coveo.com

www.datomic.com

www.eventstore.com

Technical documentation

docs.aws.amazon.com/redshift

docs.coveo.com

docs.datomic.com

developers.eventstore.com

Developer

Amazon (based on PostgreSQL)

Coveo

Cognitect

Event Store Limited

Initial release

2012

Current release

1.0.7180, July 2024

21.2, February 2021

License

Commercial or Open Source

commercial

limited edition free

Open Source

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, Clojure

Server operating systems

hosted

All OS with a Java VM

Linux
Windows

Data scheme

yes

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

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

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C#
Java
JavaScript
Python

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

Triggers

yes

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

none

But extensive use of caching in the application peers

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

In-memory capabilities

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

yes

recommended only for testing and development

User concepts

Access control

fine grained access rights according to SQL-standard

granular access controls, API key management, content filters

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