vault backup: 2026-07-23 07:56:59

2026-07-23 07:56:59 -04:00
parent 1033a565cb
commit af0951d4db
11 changed files with 1355 additions and 0 deletions
@@ -0,0 +1,197 @@
+# CTO Academy - Data
+
+This is the canonical reading order for the Data module.
+
+## Progress
+
+- [ ] Lesson 01 - [[Medallion Architecture]]
+- [ ] Lesson 02 - [[Data Lake vs Data Warehouse vs Lakehouse]]
+- [ ] Lesson 03 - [[ETL vs ELT]]
+- [ ] Lesson 04 - [[Fact Table]]
+- [ ] Lesson 05 - [[Dimension Table]]
+- [ ] Lesson 06 - [[Star Schema]]
+- [ ] Lesson 07 - [[Snowflake Schema]]
+- [ ] Lesson 08 - [[Semantic Layer]]
+- [ ] Lesson 09 - [[Data Lineage]]
+- [ ] Lesson 10 - [[Data Governance]]
+- [ ] Lesson 11 - [[Data Quality]]
+- [ ] Lesson 12 - [[Metadata]]
+- [ ] Lesson 13 - [[Data Catalog]]
+- [ ] Lesson 14 - [[Data Owner]]
+- [ ] Lesson 15 - [[Data Steward]]
+- [ ] Lesson 16 - [[Business Glossary]]
+- [ ] Lesson 17 - [[KPI]]
+- [ ] Lesson 18 - [[Master Data Management]]
+- [ ] Lesson 19 - [[Data Observability]]
+- [ ] Lesson 20 - [[Data Contract]]
+
+---
+
+## Phase 1 - Data Architecture Foundations
+
+### Lesson 01 - [[Medallion Architecture]]
+
+Understand the Bronze → Silver → Gold model and how data matures through the platform.
+
+### Lesson 02 - [[Data Lake vs Data Warehouse vs Lakehouse]]
+
+Understand the major analytical storage architectures and when each is appropriate.
+
+### Lesson 03 - [[ETL vs ELT]]
+
+Understand where transformation happens in the data pipeline and why modern architectures increasingly use ELT.
+
+---
+
+## Phase 2 - Analytical Data Modeling
+
+### Lesson 04 - [[Fact Table]]
+
+Understand how business events and measurements are represented.
+
+### Lesson 05 - [[Dimension Table]]
+
+Understand how descriptive context is attached to business events.
+
+### Lesson 06 - [[Star Schema]]
+
+Understand the standard dimensional model used by analytical and BI systems.
+
+### Lesson 07 - [[Snowflake Schema]]
+
+Understand normalization of dimensions and the trade-off between simplicity and reduced duplication.
+
+---
+
+## Phase 3 - Business Meaning and Trust
+
+### Lesson 08 - [[Semantic Layer]]
+
+Understand how organizations create shared and reusable business definitions.
+
+### Lesson 09 - [[Data Lineage]]
+
+Understand where data comes from, how it changes, where it goes, and how to perform impact analysis.
+
+### Lesson 10 - [[Data Governance]]
+
+Understand the people, policies, ownership, standards, and processes that make organizational data trustworthy.
+
+---
+
+## Phase 4 - Operationalizing Data Governance
+
+### Lesson 11 - [[Data Quality]]
+
+Understand how accuracy, completeness, consistency, timeliness, validity, and uniqueness are measured.
+
+### Lesson 12 - [[Metadata]]
+
+Understand the information that describes datasets, fields, ownership, refresh schedules, and business meaning.
+
+### Lesson 13 - [[Data Catalog]]
+
+Understand how organizations make datasets, metrics, lineage, ownership, and metadata discoverable.
+
+### Lesson 14 - [[Data Owner]]
+
+Understand who is accountable for the business meaning and appropriate management of data.
+
+### Lesson 15 - [[Data Steward]]
+
+Understand who handles the day-to-day operational responsibilities of governed data.
+
+### Lesson 16 - [[Business Glossary]]
+
+Understand how an organization establishes shared definitions for important business concepts.
+
+---
+
+## Phase 5 - Managing Data as a Business Asset
+
+### Lesson 17 - [[KPI]]
+
+Understand the difference between measures, metrics, KPIs, outputs, outcomes, and leading/lagging indicators.
+
+### Lesson 18 - [[Master Data Management]]
+
+Understand how organizations establish authoritative identities for important entities across systems.
+
+### Lesson 19 - [[Data Observability]]
+
+Understand how data teams detect broken, stale, incomplete, or abnormal data before business users discover it.
+
+### Lesson 20 - [[Data Contract]]
+
+Understand how producers and consumers establish explicit expectations for shared data.
+
+---
+
+# Quizzes
+
+- [[Quiz 01 - Data Foundations]]
+
+---
+
+# Mental Model
+
+The technical path:
+
+Source Systems
+↓
+[[ETL vs ELT]]
+↓
+[[Medallion Architecture]]
+↓
+Bronze → Silver → Gold
+↓
+[[Fact Table]] + [[Dimension Table]]
+↓
+[[Star Schema]] / [[Snowflake Schema]]
+↓
+[[Semantic Layer]]
+↓
+Power BI
+↓
+Business Decisions
+
+The governance surrounding it:
+
+[[Data Governance]]
+├── [[Data Owner]]
+├── [[Data Steward]]
+├── [[Business Glossary]]
+├── [[Metadata]]
+├── [[Data Catalog]]
+├── [[Data Quality]]
+├── [[Data Lineage]]
+└── [[Data Contract]]
+
+The operational trust layer:
+
+[[Data Quality]]
+
+[[Data Lineage]]
+
+[[Data Observability]]
+↓
+Can we trust the data right now?
+
+---
+
+# CTO Questions to Remember
+
+When presented with data, ask:
+
+1. What business question are we trying to answer?
+2. What does one row represent? → [[Fact Table]]
+3. What is the [[Grain]]?
+4. What dimensions provide context? → [[Dimension Table]]
+5. What is the business definition? → [[Business Glossary]]
+6. Who owns that definition? → [[Data Owner]]
+7. Is the definition implemented consistently? → [[Semantic Layer]]
+8. Where did the data come from? → [[Data Lineage]]
+9. Can we trust it? → [[Data Quality]]
+10. How would we know if it broke? → [[Data Observability]]
+11. What happens downstream if the source changes? → [[Data Lineage]]
+12. What are producers expected to guarantee? → [[Data Contract]]