Beginner PySpark Engineering Lessons

I am starting from scratch in data engineering and want a structured, hands-on learning path that begins with PySpark. Video-based instruction is my preferred format, so each topic should arrive as clear, well-paced screen-recorded sessions I can replay and code along with.

Phase one is all about mastering PySpark: setting up a local environment, understanding RDDs and DataFrames, writing efficient transformations, and pushing small projects to Git. Once that foundation feels solid we will branch into the rest of the modern stack called out in the brief—data-cleaning best practices, Kafka streaming pipelines, Databricks workflows, Snowflake integrations, and the core AWS services (S3, Glue, Lambda, Redshift) that tie everything together.

For every module I expect:
• Short, goal-driven videos (10–20 min segments)
• Companion notebooks or .py files that reproduce what I see on screen
• A quick challenge exercise with a walkthrough solution

I will consider the training a success when I can independently build and deploy a small end-to-end pipeline—from raw data in S3, through PySpark processing on Databricks, into a Snowflake table, and finally exposed via a Kafka topic—while following the best practices you demonstrate.

If you have produced similar tutorial content before, feel free to share a sample clip or repository link so I can gauge the teaching style. Looking forward to learning!