------------------------------------| 1. DATA SOURCES |
| CSVs, APIs, OLTP database |
| exports, and Kaggle warehouse |
| benchmarks like TPC-DS populate |
| pipeline. Partner feeds and |
| internal logs supplement data. |
| Validation checks ensure schema |
| consistency and freshness. |
------------------------------------ | v------------------------------------| 2. INGESTION & ORCHESTRATION |
| (EXTRACT) |
| Airflow DAGs schedule ingestion |
| jobs that clean, dedupe, and |
| load raw data into staging |
| layers. Data lineage and |
| observability ensure reliability.|
| Failure recovery mechanisms |
| guarantee stable data flow. |
------------------------------------ | v------------------------------------| 3. TRANSFORMATION (TRANSFORM - |
| dbt Modeling) |
| Modular SQL models apply tests, |
| documentation, and version |
| control. dbt enables reproducible|
| transformations. Business logic |
| centralized to reduce debt and |
| improve interpretability. |
------------------------------------ | v------------------------------------| 4. WAREHOUSE STORAGE & MODELING |
| (LOAD) |
| Processed data lands in |
| Snowflake/BigQuery with scalable |
| compute. Fact and dimension |
| tables support BI workloads. |
| Materialized views accelerate |
| dashboard queries. |
------------------------------------ | v-------------------------------------| 5. BI ANALYTICS & SELF-SERVICE |
| (ANALYZE) |
| Power BI, Looker, and Superset |
| dashboards offer visual analytics.|
| Business users explore curated |
| datasets. Performance tuning |
| ensures cost-efficient |
| computation. |
------------------------------------- | v------------------------------------| 6. MONITORING & FEEDBACK LOOP |
| (MONITOR) |
| Data quality metrics track |
| anomalies. Usage analytics |
| highlight trends. User feedback |
| refines models. Pipeline |
| adjustments and schema updates |
| evolve with business needs. |
------------------------------------