Express a testable rule
Define eligibility, assertion, severity and repair evidence for each check.
Python and data workflows · EDITION 1.0.0
Write Data Quality Rules That Explain Failures
Implement an inspectable shipment-quality engine that separates field validity, cross-field consistency, reference checks and overdue warnings.
By Sanjaay Gowda T S · For analysts and python developers who need quality checks that help people repair source data.
You need: Basic Python functions and dictionaries; JSON arrays and objects; Calendar date basics
Runtime and hardware details
Python 3.10 or later and a terminal. The supplied project runs locally and uses the Python standard library.
No model, API key, cloud account or network calls are used by the companion.
Basic Python functions and dictionaries; JSON arrays and objects; Calendar date basics.
Full setup and scope ↓$2.99USD · one-time purchase
Prepared with AI assistance. Independent human technical, editorial and assistive-technology reader review remains pending. Automated checks and agent review are not certification.
PDF + EPUB + companion project. Any applicable tax is shown at checkout. Single-reader ebook terms; companion code is MIT licensed.
TRY THE FREE SAMPLE
See how the guide teaches.
Read 6 sample pages to explore the introduction, first chapter and teaching approach before you choose the complete edition.
Read free sample PDF · No checkout required
WHAT YOU WILL LEARN
Understand each part.
Inspect every boundary.
Respect dependencies
Skip comparisons whose prerequisite fields are invalid instead of inventing more failures.
Report useful evidence
Return rule-level denominators and record-level explanations without hiding warnings.
INSIDE THE BOOK
9 focused chapters.
- Define quality for a decision
- Write a rule contract
- Validate fields before relationships
- Check identities and references
- Express cross-field consistency
- Handle time-dependent warnings
- Report denominators honestly
- Test rules and explanations
- Review changes without hiding failures
Your download includes
- 21-page tagged PDF with linked contents
- Reflowable EPUB for ebook readers
- Local Python project, fictional input, recorded expected JSON output and 14 companion tests
- 3 explanatory diagrams
- 9 chapters with exercises and worked answers
Before you begin
Python 3.10 or later and a terminal. The supplied project runs locally and uses the Python standard library.
No model, API key, cloud account or network calls are used by the companion.
Learning prerequisites: Basic Python functions and dictionaries; JSON arrays and objects; Calendar date basics.
Prepared with AI assistance. Independent human technical, editorial and assistive-technology reader review is pending.
The included examples are synthetic teaching material. Model access, software subscriptions, hosting, ongoing updates and consulting are not included.
CLEAR EXPECTATIONS
Before you buy.
What will I build?
Implement an inspectable shipment-quality engine that separates field validity, cross-field consistency, reference checks and overdue warnings. The local companion uses fictional teaching fixtures. Project limits: Small current-state snapshots only; Calendar-day route policy, no business calendars; Additional shipment fields ignored; No source completeness, event truth or operational issue workflow.
What was tested?
The 14 companion tests passed on the packaged project. The supplied fictional fixture produced its recorded expected JSON output. EPUBCheck reported no errors or warnings; DAISY Ace and PDF/UA automated checks passed. These local teaching checks are not production accuracy claims or certification. Independent human technical, editorial and assistive-technology reader review remains pending.
How do I get the files?
Complete the secure Lemon Squeezy checkout to access the PDF, EPUB and companion ZIP. Keep your receipt and download access private. This is a one-time purchase of edition 1.0.0; ongoing updates and consulting are not included.
Can I share the book or the companion code?
The full ebook is licensed to one reader. The labelled free sample may be shared unchanged. The companion project is MIT licensed and may be adapted or shared with its licence notice. See the exact ebook terms.
Build dependency-aware checks with stable reasons, useful denominators and repair evidence
One book. A complete learning project. Evidence you can inspect.
AI-assisted · Independent human review pending
$2.99USDBuy ebook