DBMS > Amazon Redshift vs. Impala vs. SQream DB

System Properties Comparison Amazon Redshift vs. Impala vs. SQream DB

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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

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Relational DBMS

Secondary database models

Document store

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

Score	20.46
Rank	#35	Overall
	#21	Relational DBMS

Score	18.95
Rank	#37	Overall
	#23	Relational DBMS

Score	0.81
Rank	#238	Overall
	#112	Relational DBMS

Website

aws.amazon.com/redshift

www.cloudera.com/products/open-source/apache-hadoop/impala.html

sqream.com

Technical documentation

docs.aws.amazon.com/redshift

docs.cloudera.com/documentation/enterprise/latest/topics/impala.html

docs.sqream.com

Developer

Amazon (based on PostgreSQL)

Cloudera

SQream Technologies

Initial release

2012

2013

2017

Current release

4.1.0, June 2022

2022.1.6, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

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++

C++, CUDA, Haskell, Java, Scala

Server operating systems

hosted

Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

APIs and other access methods

JDBC
ODBC

.Net
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

user defined functions in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

none

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

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

More information provided by the system vendor

Specific characteristics

SQream allows companies to get answers from its data and get new business-insights...
» more

Competitive advantages

Scalability when processing bigger datasets. Faster business insights, with more...
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Typical application scenarios

What can you do with 100x faster SQL analytics? Better and more timely decision-making,...
» more

Key customers

Some of our customers include: LG-U+, AIS , ACL Mobile, Pubmatic, Telkomsel, LG Electronics
» more

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