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Fabric for Data Engineers

A Retail Analytics Case Study Across OneLake, Lakehouse, and Direct Lake

For the senior data engineer landing in Microsoft Fabric who wants the engine trade-offs, OneLake mechanics, and capacity economics — not a Power BI tutorial.

10 chapters · planned · Microsoft Fabric · retail analytics case study

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Status: plannedOutline draftedWaitlist open

What you'll build

A single retail analytics estate, built progressively across all 10 chapters: sales, inventory, stores, suppliers, and promotions, landed in OneLake and served through Direct Lake.

Land

Pipelines and Dataflows Gen2 into OneLake: sales, inventory, stores, suppliers.

Refine

Lakehouse notebooks and shortcuts: cleaned, conformed, incremental.

Serve

Warehouse marts and Direct Lake semantic models: fct_sales, dim_store.

This is for you if

  • ✓You're accountable for a Fabric capacity and need to know what actually consumes it.
  • ✓You want a straight answer on Lakehouse vs. Warehouse vs. Eventhouse for each workload.
  • ✓You care how shortcuts, mirroring, and Direct Lake behave under real data volumes.

Not for you if

  • —You're looking for a first introduction to SQL or the Microsoft data stack.
  • —You want report-building or DAX instruction — this stops at the semantic model boundary.
  • —You need Power Platform or Copilot Studio coverage.

Sample

Be first to read it

Free sample chapters go up here the day writing starts.

Email signup opens soon. Until then, write to vincent@yzcworks.com.

Contents & progress

Production ledger

The planned chapter list — statuses go live when writing starts.

ChTitleStatusCovers
01Fabric's Architecture & OneLakePlannedCapacities, workspaces, the SaaS model, what OneLake actually stores.
02Choosing an EnginePlannedLakehouse vs. Warehouse vs. Eventhouse, the decision table that survives contact.
03Ingestion: Pipelines & Dataflows Gen2PlannedCopy activities, incremental refresh, when a notebook beats both.
04Shortcuts & MirroringPlannedZero-copy access to ADLS, S3, and operational databases; the consistency fine print.
05Modeling in the LakehousePlannedSpark notebooks on Delta, medallion layering, V-Order and table maintenance.
06The Warehouse & T-SQL SurfacePlannedCross-database queries, what T-SQL is missing, warehouse-native modeling.
07Semantic Models & Direct LakePlannedDirect Lake vs. Import vs. DirectQuery, framing, fallback behavior.
08Governance & SecurityPlannedDomains, sensitivity labels, workspace roles, OneLake data access rules.
09CI/CD: Git Integration & Deployment PipelinesPlannedWorkspace Git modes, deployment rules, what still needs scripting.
10Capacity Management & OperationsPlannedCU consumption, throttling and smoothing, the Capacity Metrics app, runbooks.

Pricing

Pricing

Not yet available for purchase

Sales open when the book is finished. Waitlist subscribers get 30% off during launch week.

Standard Edition

$19.90 USD, one-time

The complete book and everything that ships with it.

  • —PDF + ePub + web (HTML) edition
  • —Templates & checklists pack
  • —Prompt pack for AI-assisted Fabric work
  • —Free updates within this major version

Purchase not yet open

Join the waitlist

How delivery works

  1. 01Checkout is handled by Lemon Squeezy, which acts as the merchant of record and handles sales tax and VAT.
  2. 02Right after payment you get a receipt email with download links for every file. The web edition is a zipped HTML folder you open in any browser. The same files stay available from your Lemon Squeezy order page.
  3. 03The case-study code is a separate public repository under the MIT license, free for everyone. It opens at launch.
  4. 04Updates within this major version are uploaded to the same order, so you download them from the same place.

Buying for a team? Email vincent@yzcworks.com for a team licence. See the refund policy and terms.

Proof

Every example runs — check for yourself

The full case-study project will be a public, runnable repository under the MIT license. It opens at launch.

https://github.com/vincentyin-data

Vincent Yin

Senior Data Engineer

Vincent Yin is a senior data engineer with 18 years building data platforms across insurance and finance — enterprise data lakes, lakehouse migrations, dbt-modeled warehouses, and most recently production AI applications on the data stack. This series distills the judgment calls — not the syntax — that separate working pipelines from production-grade ones.

How these books are made →

Refund policy & FAQ

30-day, no questions asked. Read the full policy.

Join the waitlist

Email signup opens soon. Until then, write to vincent@yzcworks.com.