DBMS > Amazon Redshift vs. Apache Impala vs. Databend vs. jBASE

System Properties Comparison Amazon Redshift vs. Apache Impala vs. Databend vs. jBASE

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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

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

A robust multi-value DBMS comprising development tools and middleware

Primary database model

Relational DBMS

Multivalue DBMS

Secondary database models

Document store

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	1.49
Rank	#156	Overall
	#3	Multivalue DBMS

Website

aws.amazon.com/redshift

impala.apache.org

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

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

docs.databend.com

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Databend Labs

Rocket Software (formerly Zumasys)

Initial release

2012

2013

2021

1991

Current release

4.1.0, June 2022

1.0.59, April 2023

5.7

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

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

C++

Rust

Server operating systems

hosted

Linux

hosted
Linux
macOS

AIX
Linux
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

optional

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

yes

Embedded SQL for jBASE in BASIC

APIs and other access methods

JDBC
ODBC

CLI Client
JDBC
RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
JavaScript (Node.js)
Python
Rust

.Net
Basic
Jabbascript
Java

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

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

none

yes

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

yes

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

Users with fine-grained authorization concept, user roles

Access rights can be defined down to the item level

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