- Description
- A delivery truck
- Source
- George Chernilevsky · original page
- Licence
- PD Public domain
Project Management, Tools, Maturity, and Power BI Reporting
This section is a set of interconnected educational guides. It answers a simple question: how to manage, equip, and measure a data and BI project, from scoping to…
In brief
This section is a set of interconnected educational guides. It answers a simple question: how to manage, equip, and measure a data and BI project, from scoping to production rollout, with a particular focus on the SQL Server / Power BI pairing and the arrival of Microsoft Fabric. Each file steps up in depth: fundamentals (🟢), practical (🟡), expert (🔴). After reading the portal, you'll know where to go based on your profile and available time.
Level: 🟢→🔴
The promise#
Three commitments:
- Substance, not surface. Precise definitions, mechanisms, trade-offs, counterexamples, real code snippets (SQL, DAX, PowerShell, git, tf.exe).
- Explicit progression. In each file: 🟢 novice → 🟡 practitioner → 🔴 expert. You can stop wherever you want.
- Quantified and honest judgments. Comparisons include /5 ratings by criterion with a one-sentence justification; these are expert assessments, contextual, explained in RATING. Volatile facts (Fabric, Power BI, Azure DevOps, Copilot, licenses) are dated “(as of 2026-10)” or marked “to be verified”: no statistic is made up.
The content is dated October 2026. A cloud feature can move from preview to GA, or change its name, within a few months: recheck before basing an architecture or budget decision on a line from this site.
Who this section is for#
| Reader | What they are looking for | Where to start |
|---|---|---|
| 🟢 Novice (junior, new to the field) | Understand vocabulary and major method families | Glossary, Project Management Fundamentals |
| 🟡 Practitioner (BI/datamart developer, project manager) | Choose, equip, industrialize | Tools, Power BI Reporting |
| 🔴 Expert / team lead | Measure maturity, arbitrate, train | Maturity Matrices |
The reference reader is an experienced BI/datamart developer (SQL Server / Azure SQL, Power BI, TFVC/Git version control, PowerShell) who also wants to train juniors and raise their team's maturity.
Plan of the five folders#
00 — Read First#
How-to guide, conventions, and reading paths.
- Folder README — how to use this section
- RATING — how to read /5 scores, the matrix rating rule, the 🟢🟡🔴 scale, and callout boxes
- READING PATHS — paths by profile and by duration (1 day, 1 week, 1 month)
01 — Project Management#
Methods, from the most predictive to the most agile, their application to data, and continuous delivery. Entry point: Folder README.
02 — Tools#
Version control, planning, steering. Entry point: Folder README.
| File | Topic |
|---|---|
| 01 — Version Control Systems Overview | Version control systems |
| 02 — Git In Depth | Object model, branches, merges, hooks |
| 03 — TFVC In Depth | Workspaces, changesets, shelvesets, tf.exe |
| 04 — Git vs TFVC: Compare, Migrate, and Coexist | Compare, migrate, coexist |
| 05 — Branching Strategies | Trunk-based, GitFlow, releases |
| 06 — Version Control for Data: SQL and Power BI | Version databases, TMDL, encoding, line endings |
| 07 — Planning Tools Comparison | Overview |
| 08 — Azure DevOps Server (Formerly TFS) In Depth | Work items, pipelines, API |
| 09 — Jira In Depth and Alternatives | Jira in depth |
| 10 — Project Metrics and Reporting | DORA, flow, velocity |
| 11 — AI in Planning and Version Control Tools | AI in planning and version control management |
| 12 — Planning Tools Comparison | /5 ratings |
03 — Maturity Matrices#
Four grids to measure a practice: each theme has five levels, a promise, and verifiable milestones. Entry point: Folder README · Self-Assessment with Maturity Matrices.
| Matrix | Code | Themes | Perspective |
|---|---|---|---|
| Maturity Matrix — IT Project Management (PI) | PI | 11 | Steering, governance, human factors |
| Maturity Matrix — Software Development (DL Code) | DL | 12 | Code and delivery engineering |
| Maturity Matrix — Data Project Management (PD) | PD | 12 | Data as an asset and as a project |
| Maturity Matrix — BI Tool/Application Development | BI | 12 | The BI product: model, DAX, RLS, reporting |
Key themes to get started: Data Quality, Semantic Model, Testing, Risk Management (RSK).
04 — Power BI Reporting#
Two architectures compared, with challenges, pitfalls, and AI. Entry point: Folder README.
| File | Topic |
|---|---|
| 01 — OneLake + SaaS Mobile Apps: Overview | Fabric/OneLake fed by SaaS mobile applications |
| 02 — Architecture | Lakehouse, Direct Lake, mirroring, pipelines |
| 03 — Challenges and Pitfalls | Quotas, schema drift, offline |
| 04 — AI Contribution | Use cases and guardrails |
| 05 — SQL Server in SaaS — Overview | Azure SQL, Managed Instance, SQL database in Fabric |
| 06 — Architecture | Import, DirectQuery, composite |
| 07 — Challenges and Pitfalls | Performance, gateway, security |
| 08 — AI Contribution | Use cases and guardrails |
| 09 — Comparison — OneLake/Fabric vs Managed SQL Server for Power BI Reporting | /5 ratings |
| 10 — Cross-Cutting Power BI Best Practices | Model, DAX, security, operations |
| 11 — Power BI Reporting Project Checklists | Operational checklists |
05 — Cross-Cutting#
- Cross-Cutting Overview — AI Contribution — what AI changes, everywhere
- Bibliography and Resources — sources, reading, tools
And at the root: the Glossary (more than 90 terms, each linked to the file that explores it in depth).
Recommended paths#
Details are in READING PATHS. Shortcuts:
| If you have… | Do… |
|---|---|
| 1 hour | RATING, then Project Management Methods Comparison |
| 1 day | Project Management Fundamentals → Scrum Applied to Data, BI, and Datamarts → Git vs TFVC: Compare, Migrate, and Coexist → Comparison — OneLake/Fabric vs Managed SQL Server for Power BI Reporting |
| 1 week | One folder per day, in plan order, with the next day's matrix skimmed in advance |
| 1 month | Full path, then self-assessment and improvement plan |
Reading conventions#
- Level markers: 🟢 novice, 🟡 practitioner, 🔴 expert, placed in section titles.
- Callouts: ✅ good practice · ⚠️ pitfall · 💡 expert tip · 🧪 quantified example · 🤖 AI contribution.
- /5 ratings: always accompanied by context and a one-sentence justification; they are judgments, not measurements. See RATING.
- Links: relative, between files; key technical terms link to the Glossary at their first important occurrence.
- Dates: every volatile fact is marked “(as of 2026-10)” or “to be verified”.
- Footer: editorial signature, because this content is written by an AI at the request of a human responsible for its use.
- Encoding: accented French text, UTF-8 without BOM, LF line endings.
Common thread and limits#
The examples are based, anonymously, on “a SQL Server datamart fed by a field sales force automation (SFA) application, with multilingual Power BI reports, versioned in TFVC, delivered as installation kits.” No real client is named. This is only a guiding thread: the principles apply beyond it.
These folders were written by an AI. They are a solid starting point, not an authority: orders-of-magnitude figures are explicitly marked as such, and any high-impact decision (licenses, architecture, compliance) deserves verification against the official sources listed in the bibliography.
See also#
✻ Written by Claude Sonnet 5.5
