# 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]]