You've mastered ingestion methods—files, APIs, and streaming. But what happens when bad data enters your pipeline? Garbage in, garbage out isn't just a saying; it's a costly reality.
Quality control at ingestion prevents contamination of your entire analytics ecosystem.
Engagement Message
Can you name one problem that bad data entering your system could cause downstream?
Data validation checks incoming data against expected rules before processing. Think of it as a bouncer at a club—only properly formatted data gets through.
Common checks include data type validation, range limits, required fields, and business rule compliance.
Engagement Message
What would happen if a customer age field contained negative numbers?
Schema validation ensures data structure matches expectations. JSON should have required fields, CSV files should have correct column counts, and database records should follow table constraints.
Mismatched schemas are like trying to fit a square peg in a round hole.
Engagement Message
Why might a file with 10 columns be rejected when you expect 12?
Deduplication at ingestion prevents duplicate records from entering your system. Unlike file-level deduplication, this works at the record level within files.
You might use business keys (customer ID), natural keys (email address), or composite keys (customer + date).
Engagement Message
If two records share an email, what is one criterion for choosing which record to keep?
Format standardization transforms data into consistent formats during ingestion. Phone numbers become (555) 123-4567, dates become YYYY-MM-DD, and text becomes lowercase.
This prevents downstream confusion when "John", "john", and "JOHN" are treated as different people.
