1. 🗺 Data Lineage
1.1. Upstream
1.1.1. Manual Lineage Definition
1.1.2. Integrations
1.2. Dependency DAG
1.3. Downstream
1.3.1. Data Owner
1.3.2. Impacted Business Processes
2. 🏛 Data Governance
2.1. Data Owner
2.2. Data Stakeholder Map
3. 👀 Data Discovery/Experience
3.1. Web UI - amora[dash]
3.1.1. CLI amora dash serve
3.1.2. Model Details
3.1.2.1. Columns Description
3.1.2.1.1. Column Description Dictionary
3.1.2.2. Data Labels
3.1.2.3. Data Freshness
3.1.2.4. Data Sample
3.1.2.4.1. Head
3.1.2.4.2. Random Sampling
3.1.2.4.3. Tail
3.1.2.5. AmoraModel Python source code
3.1.2.6. AmoraModel SQL source code
3.1.2.7. Data Quality Badge
3.1.3. Feature Store
3.1.3.1. Feature View List
3.1.3.1.1. Feature View Details
3.1.4. Data Questions
3.1.4.1. Dashboards
3.1.4.1.1. Link Sharing
3.1.4.2. Link Sharing
3.1.4.3. Question Moderation
3.1.4.4. Data Observability
3.1.4.5. Alerts
3.1.4.6. Filters
3.1.4.6.1. Auto DateFilter from partition field
3.1.4.6.2. Auto Filters from cluster fields
3.1.5. Audit Logs
3.1.6. DataFrame Caching
3.1.7. User Colaboration
3.1.7.1. Authentication
3.1.7.1.1. Auth0
3.1.7.2. Authorization
3.1.7.3. Browser Based IDE
3.1.7.3.1. Git Integration
3.1.7.3.2. Query Execution
3.1.7.3.3. CLI Execution
3.1.7.3.4. AmoraModel CRUD
3.1.7.4. No-code Data Question
3.1.8. Drag-n-Drop Pivot Tables
3.1.9. Data Stakeholder Map
3.2. Webhooks
3.3. Activity Feed
3.3.1. Failed test assertion
3.3.2. Data Model schema change
3.3.3. Data Model creation
3.3.4. Data Owner change
4. 🧪 Data Quality
4.1. Data assertions with pytest
4.2. Data Metrics/Metadata
4.2.1. Summary
4.2.1.1. Count
4.2.1.2. Distinct Count
4.2.1.3. Min
4.2.1.4. Max
4.2.1.5. Avg
4.2.1.6. Sum
4.2.1.7. Stardard Deviation
4.2.1.8. Null percentage
4.2.1.9. Variance
4.2.1.10. Approximated Distinct Count
4.2.1.11. Min length
4.2.1.12. Max length
4.2.1.13. Missing percentage
4.2.2. Summary History
4.2.3. Anomalies Detection
4.2.4. Schema Changes
4.3. CLI amora test
4.3.1. Test Parallelism
4.4. Test History
4.5. Column level test coverage
5. 🏗 Data Transformation
5.1. Python to SQL
5.1.1. CLI amora compile
5.2. Big Query to AmoraModel
5.2.1. CLI amora models import
5.3. Data pipelines
5.3.1. CLI amora materialize
5.3.2. Materialization Parallelism
5.4. Feature Store - amora[feature-store]
5.4.1. Online Store (Redis)
5.4.1.1. CLI amora feature-store materialize
5.4.2. Offline Store (Big Query)
5.4.3. Feature Serving
5.4.3.1. HTTP API
5.4.3.1.1. Prometheus metrics
5.4.3.1.2. CLI amora feature-store serve
5.4.3.2. Python SDK
6. 💵 Cost
6.1. Cost Estimation in USD
6.1.1. Data Model Storage Cost
6.1.2. Query cost
6.1.3. Online Store Cost estimation
6.1.3.1. Cost / Request