DBMS > Amazon Redshift vs. Databend vs. Ingres vs. Kinetica

System Properties Comparison Amazon Redshift vs. Databend vs. Ingres vs. Kinetica

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Well established RDBMS

Fully vectorized database across both GPUs and CPUs

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	0.28
Rank	#287	Overall
	#131	Relational DBMS

Score	3.63
Rank	#79	Overall
	#43	Relational DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Website

aws.amazon.com/redshift

github.com/datafuselabs/databend
www.databend.com

www.actian.com/databases/ingres

www.kinetica.com

Technical documentation

docs.aws.amazon.com/redshift

docs.databend.com

docs.actian.com/ingres

docs.kinetica.com

Developer

Amazon (based on PostgreSQL)

Databend Labs

Actian Corporation

Kinetica

Initial release

2012

2021

1974

originally developed at University Berkely in early 1970s

2012

Current release

1.0.59, April 2023

12.0, July 2024

7.1, August 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

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

Rust

C, C++

Server operating systems

hosted

hosted
Linux
macOS

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux

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.

but tools for importing/exporting data from/to XML-files available

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

CLI Client
JDBC
RESTful HTTP API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

JDBC
ODBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
JavaScript (Node.js)
Python
Rust

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Ingres Replicator

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

GPU vRAM or System RAM

User concepts

Access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

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