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