Meet Redshift, a robust columnar data warehouse. We'll explore how columnar and typical data warehouses differ, what makes Redshift unique, how it fits into the AWS and data warehouse ecosystems, and what data types it supports.

Meet Redshift, a columnar database

Options

Columnar database

Row-based database

Columnar or Row-Based

Find what schema a table is in

Learning about Redshift data types and features

Experimenting with compatible types

Redshift types

Explicitly converting types

Inside the Redshift warehouse

Leader

Compute

Node types and responsibilities

Predicates

Leader specific functions

Redshift, a data warehouse

Let's learn about some nuances with Redshift SQL syntax, a close cousin of PostgreSQL's syntax. We compare CTEs and subqueries before moving into mastering date and time, exploring window queries, and wrapping up learning how transactions affect some functions. 

Using CTEs with Redshift

Filtering with CTEs

CTE Concepts

Date and time functions

Truncating dates

Extracting date parts

Find future dates

Windowing in Redshift

Windowing to the max

Comparing periods with lag

Transactions

Transaction structure

Consistent dates in a transaction

SQL on Redshift

We'll explore how to create tables, the impact DISTKEY and SORTKEYs have on performance, external schemas/tables with Spectrum and wrap up with handling semi-structured data like JSON with Redshift.

Tables in Redshift

Creating a table and setting properties

Learning more about a table's columns

External Schemas, File, and Table Formats

Table formats vs file types

Identifying internal and external schemas

Finding the internal or external tables

Semistructured data

Extracting top level JSON keys

Extracting nested JSON keys

Tables and working with semi-structured data

It is time to peak into the world of writing optimized queries and tools available to help optimize. Then, we turn our attention to Redshift security capabilities and how they can impact query output.

Writing optimal queries

DISTKEY and SORTKEY

Optimized GROUP BY and ORDER BY

Understanding query performance

Evaluating filter performance

EXPLAINing query behavior

Redshift security

Checking for column-level security

Checking for row-level security

Identifying dynamic data masking

Congratulations

Optimization and Security

This course offers a foundational understanding of Amazon Redshift, a distributed columnar database within the AWS cloud. Explore its integration into the AWS ecosystem and its SQL constructs, including common table expressions and analytics queries. Learn to create tables, manage data distribution, handle raw data files, and extract information from JSON. Additionally, optimize queries, tune performance, and enhance data security. By the end of the course, you'll have a strong command of SQL on Redshift and the knowledge and skills needed to use Redshift effectively.

Uncover the foundations of Amazon Redshift and its integration within the AWS cloud ecosystem, along with its position among competitors.

<h2>Mastering Redshift SQL Constructs</h2>
Dive deep into Redshift SQL constructs, including common table expressions, date-time handling, and advanced analytics queries, empowering efficient data manipulation.

<h2>Efficient Data Management and Optimization</h2>
Explore data management techniques in Redshift, from creating tables to distributing and sorting data, along with optimizing queries for enhanced performance.

<h2>Enhancing Security and Advanced Capabilities</h2>
Delve into Redshift's security features and advanced capabilities, including leveraging external schemas for raw data files and extracting insights from semi-structured data like JSON.

<h2>Commanding SQL on Redshift</h2>
Develop a strong command of SQL on Redshift, enabling effective utilization and maximizing its potential within your data ecosystem.

Data Manipulation in SQL

Master Amazon Redshift's SQL, data management, optimization, and security.

Introduction to Redshift

Predicates

Introduction to Redshift

Exercice interactif pratique