AWS Redshift – The Complete Masterclass

Master Redshift end-to-end: COPY, Spectrum, Data API, Datashare, Federated Query, Kinesis, Operations, ML, Zero-ETL

What you’ll learn
Set up Amazon Redshift (Serverless + Provisioned) with the right IAM roles, networking basics, and KMS encryption
Load data into Redshift using COPY (S3, manifests, compression, JSON/Parquet patterns, and error handling)
Automate Redshift loads with the Redshift Job Scheduler and EventBridge-style orchestration patterns
Build ELT workflows in Redshift using stored procedures (atomic/non-atomic), MERGE, UDFs, and materialized views
Use the Redshift Data API with AWS Lambda and boto3 to run SQL securely without long-lived connections
Query S3 data using Redshift Spectrum by creating external tables with the AWS Glue Data Catalog
Create views and join external Spectrum tables with internal Redshift tables for analytics workflows
Share live data using Redshift Datashare, including cross-account sharing patterns and permissions
Use Federated Query to access data in RDS/Aurora from Redshift and understand performance tradeoffs
Ingest streaming data into Redshift using Kinesis-based patterns and design for near real-time analytics
Troubleshoot performance issues: WLM queue waits, spills, skew, and concurrency bottlenecks in real workloads
Apply operational best practices: VACUUM/ANALYZE/GRANT security design, and production monitoring habits

Requirements
Basic SQL (SELECT, JOIN, GROUP BY) is helpful
Basic AWS familiarity (console navigation) is helpful but not required
An AWS account to do the hands-on labs (Free Tier where applicable; some services may incur small charges)
A laptop/desktop with internet
Optional: basic Python knowledge if you want to follow the Lambda + boto3 Data API demos (I explain it step-by-step)
No prior Redshift experience needed — we start from fundamentals and build up to production workflows.

Description
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 boto3Redshift Spectrum (external tables and views on S3 using the Glue Data Catalog)External schemas and querying external dataCross-account data sharing (Datashare)Federated queries to RDSStreaming ingestion using KinesisFinally, 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.

Who this course is for
Data engineers / analytics engineers who want production-ready Amazon Redshift skills
SQL-heavy analysts who want to understand ingestion, performance, and real AWS integrations (Spectrum, Data API, Datashare)
AWS developers building pipelines with S3, Glue, Lambda, Kinesis, and Redshift
Anyone preparing for interviews or job work involving COPY, Spectrum, performance tuning, and Redshift architecture

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