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Introduction to Redshift

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

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4 heures14 vidéos45 exercices

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Description du cours

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

    Gratuit

    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.

    Jouez Au Chapitre Maintenant
    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.

    Jouez Au Chapitre Maintenant
    Using CTEs with Redshift
    50 xp
    Filtering with CTEs
    100 xp
    CTE Concepts
    50 xp
    Date and time functions
    50 xp
    Truncating dates
    100 xp
    Extracting date parts
    100 xp
    Find future dates
    100 xp
    Windowing in Redshift
    50 xp
    Windowing to the max
    100 xp
    Comparing periods with lag
    100 xp
    Transactions
    50 xp
    Transaction structure
    100 xp
    Consistent dates in a transaction
    100 xp
  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.

    Jouez Au Chapitre Maintenant
    Tables in Redshift
    50 xp
    Creating a table and setting properties
    100 xp
    Learning more about a table's columns
    100 xp
    External Schemas, File, and Table Formats
    50 xp
    Table formats vs file types
    100 xp
    Identifying internal and external schemas
    100 xp
    Finding the internal or external tables
    100 xp
    Semistructured data
    50 xp
    Extracting top level JSON keys
    100 xp
    Extracting nested JSON keys
    100 xp
  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.

    Jouez Au Chapitre Maintenant
    Writing optimal queries
    50 xp
    DISTKEY and SORTKEY
    100 xp
    Optimized GROUP BY and ORDER BY
    100 xp
    Understanding query performance
    50 xp
    Evaluating filter performance
    50 xp
    EXPLAINing query behavior
    100 xp
    Redshift security
    50 xp
    Checking for column-level security
    100 xp
    Checking for row-level security
    100 xp
    Identifying dynamic data masking
    50 xp
    Congratulations
    50 xp
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collaborateurs

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

prérequis

Data Manipulation in SQL
Jason Myers HeadshotJason Myers

Co-Author of Essential SQLAlchemy and Software Engineer

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