DBMS > Amazon Redshift vs. atoti vs. Spark SQL vs. STSdb

System Properties Comparison Amazon Redshift vs. atoti vs. Spark SQL vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Spark SQL is a component on top of 'Spark Core' for structured data processing

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

aws.amazon.com/redshift

atoti.io

spark.apache.org/sql

github.com/STSSoft/STSdb4

Technical documentation

docs.aws.amazon.com/redshift

docs.atoti.io

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Amazon (based on PostgreSQL)

ActiveViam

Apache Software Foundation

STS Soft SC

Initial release

2012

2014

2011

Current release

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

free versions available

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

Server operating systems

hosted

Linux
OS X
Windows

Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

SQL

Support of SQL

yes

does not fully support an SQL-standard

Multidimensional Expressions (MDX)

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

.NET Client API

Supported programming languages

All languages supporting JDBC/ODBC

Java
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

user defined functions

in Python

Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

yes, multi-version concurrency control (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

User concepts

Access control

fine grained access rights according to SQL-standard

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