DBMS > Amazon Redshift vs. HEAVY.AI vs. InterSystems Caché vs. RDFox

System Properties Comparison Amazon Redshift vs. HEAVY.AI vs. InterSystems Caché vs. RDFox

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Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

Large scale data warehouse service for use with business intelligence tools

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

A multi-model DBMS and application server

High performance knowledge graph and semantic reasoning engine

Primary database model

Relational DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Graph DBMS
RDF store

Secondary database models

Spatial DBMS

Document store

Relational DBMS

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	0.24
Rank	#309	Overall
	#25	Graph DBMS
	#14	RDF stores

Website

aws.amazon.com/redshift

github.com/heavyai/heavydb
www.heavy.ai

www.intersystems.com/products/cache

www.oxfordsemantic.tech

Technical documentation

docs.aws.amazon.com/redshift

docs.heavy.ai

docs.intersystems.com

docs.oxfordsemantic.tech

Developer

Amazon (based on PostgreSQL)

HEAVY.AI, Inc.

InterSystems

Oxford Semantic Technologies

Initial release

2012

2016

1997

2017

Current release

5.10, January 2022

2018.1.4, May 2020

6.0, Septermber 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

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++ and CUDA

C++

Server operating systems

hosted

Linux

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

Linux
macOS
Windows

Data scheme

yes

depending on used data model

yes

RDF schemas

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.

yes

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Thrift
Vega

.NET Client API
JDBC
ODBC
RESTful HTTP API

RESTful HTTP API
SPARQL 1.1

Supported programming languages

All languages supporting JDBC/ODBC

All languages supporting JDBC/ODBC/Thrift
Python

C#
C++
Java

C
Java

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

Source-replica replication

replication via a shared file system

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

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

Roles, resources, and access types

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