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🛡 Free course

Quality, Testing & Ops

Be the person whose numbers people trust.

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What you will learn

  1. The six data quality dimensions
    “The data is wrong” is not a bug report. “Ride fares are 4% duplicated since Tuesday” is — and the six dimensions are how you get from one to the other.
  2. Testing pipelines: unit, integration, contract, data
    Code tests prove your logic is right. Data tests prove today's input didn't break it. A pipeline needs both, because the input changes every night and the code doesn't.
  3. Great Expectations and dbt tests in practice
    A dbt test is just a SELECT that should return zero rows. Once you see that, every test — built-in, package or hand-written — is the same idea wearing different clothes.
  4. Freshness, volume and anomaly monitoring
    The worst data incidents don't throw errors. The job succeeds, the table is valid — and it simply has 40% fewer rows than it should. Only monitoring that knows what ‘normal’ looks like can see that.
  5. Reconciliation: proving two systems agree
    Two systems that should agree never quite do. Reconciliation is the discipline of explaining every rupee of the difference — and the explained part is the bit people forget.
  6. Observability: lineage, logs, metrics, sane alerts
    Monitoring tells you something is wrong. Observability lets you ask why — without shipping new code first. At 3 AM, that difference is the whole night.
  7. On-call: triaging a broken pipeline at 3 AM
    At 3 AM your job is not to find the root cause. It's to stop bad data reaching people, tell them what's happening, and make the smallest safe change. Root cause is for daylight.
  8. Git for data teams: branching, PRs, review, CI/CD
    In application code, a bad merge breaks a feature. In a data repo, a bad merge rewrites history — every table downstream recomputes with the wrong logic. Data teams need the same Git discipline plus one extra question on every PR: what does this do to the data that already exists?

How the course works