DBMS > Amazon Redshift vs. Apache Impala vs. Dgraph vs. MonetDB

System Properties Comparison Amazon Redshift vs. Apache Impala vs. Dgraph vs. MonetDB

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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

Distributed and scalable native Graph DBMS

A relational database management system that stores data in columns

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Website

aws.amazon.com/redshift

impala.apache.org

dgraph.io

www.monetdb.org

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

dgraph.io/docs

www.monetdb.org/Documentation

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Dgraph Labs, Inc.

MonetDB BV

Initial release

2012

2013

2016

2004

Current release

4.1.0, June 2022

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Mozilla Public License 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Server operating systems

hosted

Linux

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

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-like DML and DDL statements

yes

SQL 2003 with some extensions

APIs and other access methods

JDBC
ODBC

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Synchronous replication via Raft

none

Source-replica replication available in experimental status

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

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

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Planned for future releases

fine grained access rights according to SQL-standard

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