Data Engineering / Basic: contracts and repeatable batches

Data Engineering β€” Basic: contracts and repeatable batches

Define formats, meaning, relational grain and accepted publication.

6 lessons Β· No account required

What you will learn

  • Preserve identity, types, units and expected populations.
  • Build validated and idempotent local transformations.

  1. Data flows, ownership and contracts

    Define what a production flow promises before choosing tools. Separate trustworthy delivery from analysis and connect each dataset to ownership, meaning and failure decisions.

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  2. Formats, encoding and file boundaries

    Choose interchange and analytical formats from type, access and compatibility needs. Parse quoted records correctly and distinguish a file from an atomically published dataset.

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  3. Schemas, types, nulls and time

    Preserve business meaning through explicit field types, units and temporal rules. Distinguish missing, null, zero and invalid values instead of coercing them into apparent success.

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  4. SQL grain, joins and reconciliation

    Keep relational transformations correct when one-to-many relationships change row counts. Reconcile measures at a declared grain before trusting a published total.

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  5. ETL, ELT, staging and publication

    Choose transformation placement from governance and system constraints while maintaining validation, raw-input provenance and an explicit publication boundary.

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  6. Deterministic batches and idempotent runs

    Make a rerun preserve the intended dataset state. Separate deterministic computation, event identity, run identity and atomic effect/checkpoint publication.

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