Production data is messier than test data. Someone uploads a log file with slightly different formatting, one row has a missing field, the upstream API times out. Your pipeline cannot just crash — it has to keep going, remember where it was, and tell you what failed.
So this week is basically "everything I wish I'd known when my first automation broke at 3am."
Pretty much. Five patterns that turn a prototype into a production tool — streaming reads with generators, merging two sorted timelines, chunked batch processing, retry with a sentinel, and rolling error rates for live alerting.
Retry feels like it could get complicated quickly. Is it really doable in one lesson?
The shape is simple — try, count attempts, succeed or exhaust. We'll ignore backoff and jitter for now; the core loop is five lines and every production retry system is just that loop with extras bolted on.
yield generatorrange stepping-1 on exhaustionGoal: by Friday your pipeline survives messy input, slow APIs, and chunked loads.
7 lessons this week
Production data is messier than test data. Someone uploads a log file with slightly different formatting, one row has a missing field, the upstream API times out. Your pipeline cannot just crash — it has to keep going, remember where it was, and tell you what failed.
So this week is basically "everything I wish I'd known when my first automation broke at 3am."
Pretty much. Five patterns that turn a prototype into a production tool — streaming reads with generators, merging two sorted timelines, chunked batch processing, retry with a sentinel, and rolling error rates for live alerting.
Retry feels like it could get complicated quickly. Is it really doable in one lesson?
The shape is simple — try, count attempts, succeed or exhaust. We'll ignore backoff and jitter for now; the core loop is five lines and every production retry system is just that loop with extras bolted on.
yield generatorrange stepping-1 on exhaustionGoal: by Friday your pipeline survives messy input, slow APIs, and chunked loads.