This course contains the use of artificial intelligence. This is a hands-on Amazon Redshift masterclass for SQL-heavy data engineers and analysts who want to go beyond writing queries and learn how to design, build, and operate Redshift in real production environments. It begins with Redshift fundamentals and architecture, then moves into setting up both Redshift Serverless and Provisioned correctly—covering practical foundations such as IAM, KMS, permissions (GRANT), and core workload concepts.
From there, the course focuses on real-world data ingestion using the COPY command and multiple hands on with all the different options to ingest data in Redshift. Next, it covers data processing inside Redshift with practical approaches for the all data processing offering from Redshift such as Unload, Stored Proc, Materialized views, UDF and many more.
It also covers how Redshift connects with other AWS services through hands-on examples, including:
- Redshift Data API with Lambda and boto3
- Redshift Spectrum (external tables and views on S3 using the Glue Data Catalog)
- External schemas and querying external data
- Cross-account data sharing (Datashare)
- Federated queries to RDS
- Streaming ingestion using Kinesis
Finally, it wraps up with operations and performance tuning (monitoring, skew/spills/queue waits, vacuum/analyze, concurrency tradeoffs) and finishes with modern capabilities like Redshift ML and Zero-ETL—where they fit, when they don’t, and their limitations.
By the end, you’ll have a practical, production-ready Redshift skillset you can apply immediately.
