AI ENGINEER FOUNDATIONS · STAGE 02
Data handling & engineering foundations
Move data safely and make it useful.
Learn how structured data moves from its source to reliable analytical tables. Focus on formats, SQL, transformations, data quality, and repeatable pipelines.
BEFORE YOU START
Prerequisites
- Basic programming and data structures
LEARNING OBJECTIVES
What you should be able to do
- Read common structured data formats
- Explain ETL and ELT
- Design a pipeline with validation and safe retries
WHAT YOU'LL COVER
Key concepts
SEE THE CONNECTIONS
A visual learning sequence
These are connected learning steps, not the only valid order. Revisit earlier ideas when an exercise reveals a gap.
Suggested learning sequence
Inspect the data
Understand schema, formats, ownership, and update frequency.
Why it matters: Unclear inputs create downstream assumptions and failures.
Transform deliberately
Use SQL and code to clean and model records.
Why it matters: Consumers need stable and understandable datasets.
Make it reliable
Add validation, idempotency, orchestration, and monitoring.
Why it matters: Pipelines need to recover from real operational failures.
Put it into practice
Create an orders pipeline that validates required fields, deduplicates by order ID, and safely merges late updates.
Practice tasks
- Draw an ETL and ELT flow for a warehouse.
- Define freshness and uniqueness checks for a dataset.
- Explain how a retry avoids duplicate output.
How you'll know you're ready
- Can describe where transformation happens in ETL versus ELT.
- Can name reliability controls beyond simply retrying.