DBMS > Amazon Redshift vs. Datomic vs. Firebolt vs. Kyligence Enterprise

System Properties Comparison Amazon Redshift vs. Datomic vs. Firebolt vs. Kyligence Enterprise

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

A distributed analytics engine for big data, built on top of Apache Kylin

Primary database model

Relational DBMS

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Trend Chart

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	1.90
Rank	#134	Overall
	#62	Relational DBMS

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Website

aws.amazon.com/redshift

www.datomic.com

www.firebolt.io

kyligence.io/kyligence-enterprise

Technical documentation

docs.aws.amazon.com/redshift

docs.datomic.com

docs.firebolt.io

Developer

Amazon (based on PostgreSQL)

Cognitect

Firebolt Analytics Inc.

Kyligence, Inc.

Initial release

2012

2020

2016

Current release

1.0.6735, June 2023

License

Commercial or Open Source

commercial

limited edition free

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, Clojure

Java

Server operating systems

hosted

All OS with a Java VM

hosted

Linux

Data scheme

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

yes

ANSI SQL for queries (using Apache Calcite)

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

.Net
ODBC
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

Go
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

depending on storage layer

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

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

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

yes

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

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