DBMS > Amazon Redshift vs. Apache Impala vs. Datomic vs. Immudb

System Properties Comparison Amazon Redshift vs. Apache Impala vs. Datomic vs. Immudb

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

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

An open source immutable (append-only) database with cryptographic verification which makes it tamper-resistant and fully auditable.

Primary database model

Relational DBMS

Key-value store

Secondary database models

Document store

Relational DBMS

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	1.55
Rank	#144	Overall
	#67	Relational DBMS

Score	0.24
Rank	#295	Overall
	#42	Key-value stores

Website

aws.amazon.com/redshift

impala.apache.org

www.datomic.com

github.com/codenotary/immudb
immudb.io

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

docs.datomic.com

docs.immudb.io

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Cognitect

Codenotary

Initial release

2012

2013

2012

2020

Current release

4.1.0, June 2022

1.0.7180, July 2024

1.2.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

limited edition free

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java, Clojure

Server operating systems

hosted

Linux

All OS with a Java VM

BSD
Linux
macOS
Solaris
Windows
z/OS

Data scheme

yes

schema-free

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-like DML and DDL statements

SQL-like syntax

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

gRPC protocol
PostgreSQL wire protocol

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

.Net
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

none

But extensive use of caching in the application peers

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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