Introduction to Redshift
Master Amazon Redshift's SQL, data management, optimization, and security.
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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.For Business
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Redshift, a data warehouse
FreeMeet 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 database50 xpColumnar or Row-Based100 xpFind what schema a table is in50 xpLearning about Redshift data types and features50 xpExperimenting with compatible types100 xpRedshift types50 xpExplicitly converting types100 xpInside the Redshift warehouse50 xpNode types and responsibilities100 xpPredicates50 xpLeader specific functions100 xp - 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.
Using CTEs with Redshift50 xpFiltering with CTEs100 xpCTE Concepts50 xpDate and time functions50 xpTruncating dates100 xpExtracting date parts100 xpFind future dates100 xpWindowing in Redshift50 xpWindowing to the max100 xpComparing periods with lag100 xpTransactions50 xpTransaction structure100 xpConsistent dates in a transaction100 xp - 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.
Tables in Redshift50 xpCreating a table and setting properties100 xpLearning more about a table's columns100 xpExternal Schemas, File, and Table Formats50 xpTable formats vs file types100 xpIdentifying internal and external schemas100 xpFinding the internal or external tables100 xpSemistructured data50 xpExtracting top level JSON keys100 xpExtracting nested JSON keys100 xp - 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.
Writing optimal queries50 xpDISTKEY and SORTKEY100 xpOptimized GROUP BY and ORDER BY100 xpUnderstanding query performance50 xpEvaluating filter performance50 xpEXPLAINing query behavior100 xpRedshift security50 xpChecking for column-level security100 xpChecking for row-level security100 xpIdentifying dynamic data masking50 xpCongratulations50 xp
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prerequisites
Data Manipulation in SQLJason Myers
See MoreCo-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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