SecondBrain/10 Knowledge/CTO Academy/Data/Table of Contents.md

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

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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.

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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.

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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.

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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.

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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.

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Quizzes

• Quiz 01 - Data Foundations

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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?

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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