Big data vs. bad data

Placeholder body — I’ll paste the real article here. Everything below just exercises the reading layout: headings, lists, quotes, and code.

For most of my career the industry has sold the same story: pick the right platform and your data problems go away. They don’t. The platform changes how fast you go live; it does almost nothing about the data itself.

The real enemy isn’t size

Big data is a solved-ish problem. Bad data is not. A pipeline that moves a petabyte cleanly is easier to operate than one that moves a gigabyte of inconsistent, late, half-typed records from six upstreams that all disagree.

• Schemas imposed after the fact, never at the source.
• Ingestion that silently drops malformed rows.
• “Just cast it to string” decisions that compound for years.

A query is a tradeoff, not a truth

-- Looks innocent. It's a decision about lateness vs. completeness.
SELECT user_id, COUNT(*) AS events
FROM raw.events
WHERE event_date = CURRENT_DATE   -- do late-arriving events count? you just chose.
GROUP BY user_id;

Inline code and fenced blocks should both read cleanly, because this site is going to be mostly SQL, ingestion, and pipeline talk.

What this site is for

The unglamorous groundwork — bad data, schema imposition, ingestion loss, cost, the tradeoffs each one forces. No silver bullets here.