01-projects / newsletter

dedp ingestion plan

Fri Apr 03 2026 20:00:00 GMT-0400 (Eastern Daylight Time) ·plan ·status: active

DEDP Book Ingestion Plan

Source: Patterns of Data Engineering by the DEDP author. Book is published incrementally — not all chapters are live yet. Current inventory: 20 content chapters across 2 parts (Part 3 not yet published).

Full Table of Contents

Front Matter

#TitleURL
FM-1About This Bookhttps://www.dedp.online/about-this-book.html
FM-2Introductionhttps://www.dedp.online/introduction.html
FM-3Terminologieshttps://www.dedp.online/terminologies.html

Part 1 — Foundations (History, State, and Design Pattern Concepts)

Chapter 1: Introduction to Data Engineering

#TitleURL
1.1Introduction to the Field of Data Engineeringhttps://www.dedp.online/part-1/1-introduction/_intro-data-engineering.html
1.2The History and State of Data Engineeringhttps://www.dedp.online/part-1/1-introduction/history-and-state-of-data-engineering.html
1.3Challenges in Data Engineeringhttps://www.dedp.online/part-1/1-introduction/challenges-in-data-engineering.html

Chapter 2: Overview of DEDP

#TitleURL
2.1Introduction to Data Engineering Design Patternshttps://www.dedp.online/part-1/2-overview-dedp/_intro-dedp.html
2.2Understanding Convergent Evolutionhttps://www.dedp.online/part-1/2-overview-dedp/understanding-convergent-evolution.html

Part 2 — Mastering the Patterns

Chapter 4: Convergent Evolution Examples

#TitleURL
4.1Convergent Evolution and its Patterns (Intro)https://www.dedp.online/part-2/4-ce/_example-of-convergent-evolution.html
4.2Bash-Script vs. Stored Procedure vs. Traditional ETL vs. Python-Scripthttps://www.dedp.online/part-2/4-ce/bash-stored-procedure-etl-python-script.html
4.3Data Contracts, Schema Evolution, NoSQLhttps://www.dedp.online/part-2/4-ce/data-contracts-schema-evolution-nosql.html
4.4DWH, MDM, Data Lake, Reverse ETL, CDPhttps://www.dedp.online/part-2/4-ce/dwh-mdm-data-lake-reverse-etl-cdp.html
4.5Materialized View vs. OBT vs. dbt Table vs. OLAP Cube vs. DWAhttps://www.dedp.online/part-2/4-ce/mv-obt-dbt-table-traditional-olap-dwa.html
4.6Business Intelligence, Semantic Layer, Modern OLAP, Data Virtualizationhttps://www.dedp.online/part-2/4-ce/semantic-layer-business-intelligence.html

Chapter 5: Data Engineering Patterns (DEP)

#TitleURL
5.1Data Engineering Patterns (Intro)https://www.dedp.online/part-2/5-dep/_data-engineering-patterns.html
5.2Cache Patternhttps://www.dedp.online/part-2/5-dep/cache-pattern.html
5.3Data-Asset Reusability Patternhttps://www.dedp.online/part-2/5-dep/data-asset-reusability-pattern.html
5.4Data Engineering Workspace Packaging Patternhttps://www.dedp.online/part-2/5-dep/de-workspace-packaging-pattern.html

Chapter 6: Data Engineering Design Patterns (DEDP)

#TitleURL
6.1Data Engineering Design Patterns (Intro)https://www.dedp.online/part-2/6-dedp/_data-engineering-design-patterns.html
6.2Dynamic Query Design Patternhttps://www.dedp.online/part-2/6-dedp/dynamic-queries.html

Appendix

#TitleURL
A-1Changeloghttps://www.dedp.online/appendix/changelog.html
A-2Authorhttps://www.dedp.online/appendix/author.html
A-3Feedbackhttps://www.dedp.online/appendix/feedback.html
A-4Sponsorshttps://www.dedp.online/appendix/sponsors.html

Not ingested (utility pages)

Suggested Processing Order

Priority based on relevance to active projects (phData consulting, data marketplace, newsletter content).

Batch 1 — Highest priority (architecture & modeling patterns) ✅ Complete (2026-04-04)

Direct fuel for phData consulting engagements and newsletter thought pieces.

  1. ✅ 4.4 — DWH, MDM, Data Lake, Reverse ETL, CDP → 06-reference/2026-04-04-dedp-dwh-mdm-datalake-reverse-etl-cdp
  2. ✅ 4.5 — Materialized View vs. OBT vs. dbt Table vs. OLAP Cube vs. DWA → 06-reference/2026-04-04-dedp-mv-obt-dbt-olap-dwa
  3. ✅ 4.6 — Semantic Layer, BI, Modern OLAP, Data Virtualization → 06-reference/2026-04-04-dedp-semantic-layer-bi-olap-virtualization
  4. ✅ 5.3 — Data-Asset Reusability Pattern → 06-reference/2026-04-04-dedp-data-asset-reusability-pattern

Batch 2 — Design patterns and evolution examples ✅ Complete (2026-04-04)

Core pattern thinking that differentiates our consulting and newsletter POV.

  1. ✅ 6.1 — Data Engineering Design Patterns (Intro) → 06-reference/2026-04-04-dedp-design-patterns-intro
  2. ✅ 6.2 — Dynamic Query Design Pattern → 06-reference/2026-04-04-dedp-dynamic-queries
  3. ✅ 4.2 — Bash vs. Stored Proc vs. ETL vs. Python → 06-reference/2026-04-04-dedp-etl-tool-comparisons
  4. ✅ 4.3 — Data Contracts, Schema Evolution, NoSQL → 06-reference/2026-04-04-dedp-data-contracts-schema-evolution
  5. ✅ 5.2 — Cache Pattern → 06-reference/2026-04-04-dedp-cache-pattern

Batch 3 — Foundations and frameworks ✅ Complete (2026-04-04)

Important for grounding but less immediately actionable.

  1. ✅ 2.1 — Intro to DEDP → 06-reference/2026-04-04-dedp-intro-dedp
  2. ✅ 2.2 — Understanding Convergent Evolution → 06-reference/2026-04-04-dedp-convergent-evolution
  3. ✅ 4.1 — Convergent Evolution and its Patterns (Intro) → 06-reference/2026-04-04-dedp-ce-intro
  4. ✅ 5.1 — Data Engineering Patterns (Intro) → 06-reference/2026-04-04-dedp-dep-intro
  5. ✅ 5.4 — DE Workspace Packaging Pattern → 06-reference/2026-04-04-dedp-de-workspace-packaging

Batch 4 — History, context, and front matter ✅ Complete (2026-04-04)

Background context; ingest last.

  1. ✅ FM-1 — About This Book → 06-reference/2026-04-04-dedp-about-this-book
  2. ✅ FM-2 — Introduction → 06-reference/2026-04-04-dedp-introduction
  3. ✅ FM-3 — Terminologies → 06-reference/2026-04-04-dedp-terminologies
  4. ✅ 1.1 — Intro to the Field of Data Engineering → 06-reference/2026-04-04-dedp-intro-data-engineering
  5. ✅ 1.2 — History and State of Data Engineering → 06-reference/2026-04-04-dedp-history-state-de
  6. ✅ 1.3 — Challenges in Data Engineering → 06-reference/2026-04-04-dedp-challenges-de

Batch 5 — Appendix (optional)

Only if useful for attribution or context.

  1. A-1 — Changelog
  2. A-2 — Author

Batch Summary

BatchChaptersFocus
14Architecture & modeling patterns
25Design patterns & evolution
35Foundations & frameworks
46History & context
52Appendix (optional)
Total225 batches

Notes