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Introduction to Redshift

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

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4 hours14 videos45 exercises

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Course Description

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

Mastering Redshift SQL Constructs

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

Efficient Data Management and Optimization

Explore data management techniques in Redshift, from creating tables to distributing and sorting data, along with optimizing queries for enhanced performance.

Enhancing Security and Advanced Capabilities

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.

Commanding SQL on Redshift

Develop a strong command of SQL on Redshift, enabling effective utilization and maximizing its potential within your data ecosystem.
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  1. 1

    Redshift, a data warehouse

    Free

    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.

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    Meet Redshift, a columnar database
    50 xp
    Columnar or Row-Based
    100 xp
    Find what schema a table is in
    50 xp
    Learning about Redshift data types and features
    50 xp
    Experimenting with compatible types
    100 xp
    Redshift types
    50 xp
    Explicitly converting types
    100 xp
    Inside the Redshift warehouse
    50 xp
    Node types and responsibilities
    100 xp
    Predicates
    50 xp
    Leader specific functions
    100 xp
  2. 2

    SQL on Redshift

    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.

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  3. 3

    Tables and working with semi-structured data

    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.

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  4. 4

    Optimization and Security

    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.

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collaborators

Collaborator's avatar
Arne Warnke
Collaborator's avatar
Katerina Zahradova
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Amina Edmunds

prerequisites

Data Manipulation in SQL
Jason Myers HeadshotJason Myers

Co-Author of Essential SQLAlchemy and Software Engineer

Jason Myers is a software engineer and author. His area of expertise is in developing data analytics platforms. He has also written the Essential SQLAlchemy book, co-authored with Rick Copeland, that introduces you to working with relational databases in Python.
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