GCP -Model Lifecycle

 

Google Cloud Data Model Lifecycle

         ┌────────────┐
         │ 1. DESIGN  │
         └────┬───────┘
              │
              ▼
   ┌──────────────────────────────┐
   │ • Understand business needs  │
   │ • Choose schema (Star, 3NF)  │
   │ • Identify data sources      │
   │ • Plan storage (BQ, GCS)     │
   └────────┬─────────────────────┘
            │
            ▼
        ┌─────────┐
        │ 2. BUILD│
        └───┬─────┘
            ▼
   ┌────────────────────────────────────┐
   │ • Ingest data                      │
   │   - Batch: Dataflow, Data Fusion   │
   │   - Real-time: Pub/Sub + Dataflow  │
   │ • Transform using SQL, DBT         │
   │ • Store in BigQuery                │
   │ • Model (Facts, Dimensions)        │
   └────────┬───────────────────────────┘
            │
            ▼
        ┌──────────┐
        │ 3. TEST  │
        └────┬─────┘
             ▼
   ┌────────────────────────────────────────┐
   │ • Unit test SQL models                 │
   │ • Data quality: DBT/Great Expectations│
   │ • Performance: Partitioning, EXPLAIN   │
   │ • Integration test entire pipeline     │
   └──────────┬─────────────────────────────┘
              │
              ▼
         ┌────────────┐
         │ 4. DEPLOY  │
         └────┬───────┘
              ▼
   ┌────────────────────────────────────────┐
   │ • Git + Cloud Build for CI/CD          │
   │ • Deploy DBT models to BigQuery        │
   │ • Schedule with Cloud Composer         │
   │ • Monitor with Stackdriver Logging     │
   └────────────────────────────────────────┘