Data Pipelines & Batch Fundamentals
Design repeatable extract, transform, and load workflows.
Learning objectives
By the end of this lesson, you should be able to:
- Explain the key concepts and when they are useful.
- Apply the concepts to a realistic engineering scenario.
- Identify common failure modes and choose a practical mitigation.
- Complete the assignment and explain your design decisions.
Core concepts
A data pipeline moves data from sources into a useful destination through extraction, validation, transformation, and loading. Batch processing groups records over a time window; streaming processes events continuously or in small increments. Choose based on freshness, volume, operational complexity, and failure recovery—not fashion.
Worked example
A pipeline should define source contracts, schema expectations, event time, processing time, checkpoints, retry policy, and ownership. Separate raw data from curated outputs so transformations can be rerun when business logic changes.
Engineering principles
- Make assumptions explicit. Document expected inputs, outputs, ownership, and failure behavior.
- Validate at boundaries. Treat user input, files, messages, and third-party responses as untrusted.
- Design for failure. Decide what happens when dependencies time out, data is malformed, or a process is retried.
- Prefer observable behavior. Log meaningful outcomes and expose enough diagnostics to debug without leaking secrets.
- Test the invariant. A test should prove a property that must remain true, not merely repeat the implementation.
Practical assignment
Design a daily pipeline that loads orders, validates required fields, standardizes currency, deduplicates records, and writes daily revenue aggregates.
Suggested solution and reasoning
Keep immutable raw input where permitted, validate and quarantine malformed records, make transformations deterministic, and record run metadata including input range, row counts, failures, and output version.
Use this as a reference design, not the only possible answer. A strong submission states assumptions, includes failure cases, and demonstrates how correctness will be tested.
Knowledge check
- What problem does this design solve, and what simpler alternative did you consider?
- Which input or dependency is most likely to fail?
- What invariant must remain true after a retry?
- How would you test the unhappy path?
Completion checklist
- I can explain the concepts without reading the lesson.
- I completed the practical assignment.
- I tested at least one failure scenario.
- I documented trade-offs and next steps.