Identify deliveries precisely
Distinguish path identity, content identity and event identity.
Python and data workflows · EDITION 1.0.0
Build Reliable File Ingestion Pipelines
Build a deterministic batch-ingestion planner that validates JSON Lines files, rejects ambiguous identities and distinguishes retries from changed content.
By Sanjaay Gowda T S · For python developers moving from one-off file imports to repeatable batch workflows.
You need: Python functions, exceptions and sets; Basic JSON; Running local unittest tests
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.
Python functions, exceptions and sets; Basic JSON; Running local unittest tests.
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.
Make acceptance atomic
Validate a complete file before adding any of its events.
Plan safe retries
Preserve a manifest ledger and expose conflicts instead of duplicating work.
INSIDE THE BOOK
9 focused chapters.
- Define the ingestion promise
- Separate delivery from content identity
- Validate a bounded manifest
- Parse and stage each file
- Make file acceptance atomic
- Classify retries and conflicts
- Design the durable commit boundary
- Test recovery scenarios
- Operate an exception queue
Your download includes
- 20-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: Python functions, exceptions and sets; Basic JSON; Running local unittest tests.
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?
Build a deterministic batch-ingestion planner that validates JSON Lines files, rejects ambiguous identities and distinguishes retries from changed content. The local companion uses fictional teaching fixtures. Project limits: Planner only: no filesystem reads or durable writes; UTF-8 text wrapper does not preserve original encoding bytes; Path-ordered cross-file conflict policy; No distributed exactly-once or producer authentication guarantee; Duplicate JSON object keys not rejected by this teaching decoder.
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.
Use manifests, content fingerprints and atomic file acceptance to make retries explainable
One book. A complete learning project. Evidence you can inspect.
AI-assisted · Independent human review pending
$2.99USDBuy ebook