1. Structure & Readability
- 1. Pipelines are split into logical, modular units (not one giant “do everything” job).
- 2. Transformations are grouped by purpose (staging, cleansing, business rules, loading).
- 3. Naming conventions are applied consistently for jobs, tasks, and mappings.
- 4. Comments or annotations explain non-obvious business logic.
2. Source & Target Validation
- 5. All source systems, tables, and key fields are clearly identified.
- 6. Primary keys, surrogate keys, and business keys are handled consistently.
- 7. Lookups and joins to reference data sets are defined and documented.
- 8. Referencing or filtering on deprecated fields has been removed.
- 9. Unnecessary sorts, lookups, and joins are eliminated.
- 10. Heavy transformations are pushed down to the database or Spark engine where possible.
- 11. Filters are applied as early as possible in the flow to reduce data volume.
- 12. Row-by-row or cursor-like logic is replaced by set-based operations.
4. Housekeeping & Operations
- 13. Logging exists with meaningful messages, not just success/failure flags.
- 14. Error handling paths are defined and tested (what happens when a step fails?).
- 15. The job can be restarted from a known state without reprocessing entire history unnecessarily.
- 16. Temporary objects, staging tables, and files are cleaned up per policy.
5. Data Quality & Business Validation
- 17. Critical fields have null checks, range checks, or lookup validations.
- 18. Rejected or suspect rows are captured for analysis and not silently discarded.
- 19. Key business rules are traceable to requirements or specification documents.
- 20. Aggregations and derived metrics have been reconciled against expected results.
6. Governance, Security & Controls
- 21. Sensitive data is masked, tokenized, or handled according to policy.
- 22. Control totals or row counts are checked between source and target where appropriate.
- 23. Evidence is available to show that the job aligns with internal IT control expectations.
7. Deployment & Lifecycle Readiness
- 24. Configuration (connections, paths, environment parameters) is externalized, not hard-coded.
- 25. The pipeline integrates with scheduling/orchestration (or has a clear plan to do so).
- 26. A smoke test or regression test has been defined and executed.
- 27. Ownership is clear: someone is accountable for the job after it goes live.
Running this checklist manually for every pipeline is difficult at scale. An automated code quality platform like Undraleu can enforce these standards consistently across hundreds or thousands of pipelines, turning best practices into day-to-day engineering discipline.