DBMS > Amazon Redshift vs. Apache Impala vs. Atos Standard Common Repository vs. Newts vs. RavenDB

System Properties Comparison Amazon Redshift vs. Apache Impala vs. Atos Standard Common Repository vs. Newts vs. RavenDB

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Time Series DBMS based on Cassandra

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Relational DBMS

Document store
Key-value store

Time Series DBMS

Document store

Secondary database models

Document store

Graph DBMS
Spatial DBMS
Time Series 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	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Website

aws.amazon.com/redshift

impala.apache.org

atos.net/en/convergence-creators/portfolio/standard-common-repository

opennms.github.io/newts

ravendb.net

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

github.com/OpenNMS/newts/wiki

ravendb.net/docs

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Atos Convergence Creators

OpenNMS Group

Hibernating Rhinos

Initial release

2012

2013

2016

2014

2010

Current release

4.1.0, June 2022

1703

5.4, July 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

Apache 2.0

Open Source

AGPL version 3, commercial license available

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++

Java

Server operating systems

hosted

Linux

Linux
OS X
Windows

Linux
macOS
Raspberry Pi
Windows

Data scheme

yes

Schema and schema-less with LDAP views

schema-free

Typing

predefined data types such as float or date

yes

optional

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.

yes

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

SQL-like query language (RQL)

APIs and other access methods

JDBC
ODBC

LDAP

HTTP REST
Java API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

All languages with LDAP bindings

Java

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

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

selectable replication factor

based on Cassandra

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

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

Atomic execution of specific operations

ACID, Cluster-wide transaction available

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

LDAP bind authentication

Authorization levels configured per client per database

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.