DBMS > Amazon Redshift vs. Datomic vs. EXASOL vs. LeanXcale

System Properties Comparison Amazon Redshift vs. Datomic vs. EXASOL vs. LeanXcale

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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

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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Primary database model

Relational DBMS

Key-value store
Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Website

aws.amazon.com/redshift

www.datomic.com

www.exasol.com

www.leanxcale.com

Technical documentation

docs.aws.amazon.com/redshift

docs.datomic.com

www.exasol.com/resources

Developer

Amazon (based on PostgreSQL)

Cognitect

Exasol

LeanXcale

Initial release

2012

2000

2015

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

Server operating systems

hosted

All OS with a Java VM

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

through Apache Derby

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

Java
Lua
Python
R

C
Java
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

user defined functions

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding

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

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

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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