# Crash Course to Redshift

DevFeed: [Crash Course to Redshift](<https://devfeed.tech/articles/crash-course-to-redshift-19828.md>)

Original publisher: [Read original article](<https://tech.gc.com/crash-course-to-redshift/>)

Author: GameChanger

Published: 2020-03-30T14:36:13Z

Content type: tutorial

Language: en

Sources: [GameChanger](<https://devfeed.tech/sources/gamechanger.md>)

Topics: [Amazon Redshift](<https://devfeed.tech/topics/amazon-redshift.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [data](<https://devfeed.tech/topics/data.md>), [IO](<https://devfeed.tech/topics/io.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Development](<https://devfeed.tech/topics/development.md>), [debugging](<https://devfeed.tech/topics/debugging.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [database](<https://devfeed.tech/tags/database.md>), [debugging](<https://devfeed.tech/tags/debugging.md>), [development](<https://devfeed.tech/tags/development.md>), [distributed](<https://devfeed.tech/tags/distributed.md>), [documentation](<https://devfeed.tech/tags/documentation.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [io](<https://devfeed.tech/tags/io.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [redshift](<https://devfeed.tech/tags/redshift.md>), [scale](<https://devfeed.tech/tags/scale.md>), [warehouse](<https://devfeed.tech/tags/warehouse.md>)

## AI overview

A crash course to Amazon Redshift that introduces its database architecture, large-scale table design considerations, data distribution, data loading, debugging, and query-performance optimization.

## Source excerpt

Redshift. It can store insane amounts of data. It can also store insane amounts of surprises, considerations, new ideas to learn, skewed tables to fix, distributions to get in line, what's a WLM, what am I doing‽ This post is meant to give you a crash course into working with Redshift, to get you off and running until you have the time and resources to come back and internalize what it all means. This is by no means a comprehensive review of Redshift, as then it'd no longer be a crash course, nor does this dive into data warehousing specifics, which I can cover in another post if people want. At a high level what I'll be covering is: Introduction to Redshift Table design Table analysis Data loading Debugging The vast majority of this post actually comes from our internal documentation, so you can trust that we do use this to help educate those less familiar with Redshift, and get them ramped up and feeling comfortable. Introduction to Redshift On Redshift The Redshift database will behave like other databases you've encountered, but under the hood it has some extra considerations to take into account. The main difference between Redshift and most other databases you'll have encountered is due to scale, with the cluster being important to keep in mind in table design along with standard table design considerations. And since the scale is so much larger, the impact of IO can go up considerably, especially if the cluster needs to move or share data to perform a query. The reasons for this and how to best avoid these inefficiencies are detailed below. More on Redshift database development here. On distributing data Within a Redshift cluster, there is a leader node and many compute nodes. The leader node helps orchestrate the work the compute nodes do. For example, if a query is operating only on data from May of 2017, and all of that data is stored on a single compute node, the leader only needs that node to perform the work. If instead a query is operating on data from