⭐ Cloud SQL, Firestore, Bigtable – When to Use Each
Before workflows, let’s clarify the roles:
1. Cloud SQL (MySQL / PostgreSQL / SQL Server)
Strong relational consistency
Transactions, joins, foreign keys
Great for OLTP (app data, user profiles, payments)
Strict schema
Not built for massive scale
Use for:
✓ Web apps
✓ Transactional data
✓ Reporting queries
✓ Django/Flask backends
2. Firestore (NoSQL Document Store)
Hierarchical document storage
Real-time sync
Offline caching (mobile/web)
Automatic scaling
Eventually consistent in many queries
Use for:
✓ Mobile apps
✓ Chat apps
✓ Real-time dashboards
✓ User sessions & metadata
3. Bigtable (Wide-column, petabyte scale)
Used for massive datasets
Ultra-low latency reads/writes
No SQL
Designed for time-series, IoT, ML features
Horizontally scalable
Use for:
✓ IoT sensor data
✓ Fraud detection datasets
✓ Recommendation engines
✓ Time-series data (stock prices, telemetry)
🔥 Advanced Database Workflows (Real-World Architectures)
Below are six advanced workflows used in enterprise systems.
1️⃣ Cloud SQL → Firestore Cache Layer (High-Performance Reads)
Used when Cloud SQL is too slow for high-read workloads.
Workflow:
User data stored in Cloud SQL (source of truth)
When user logs in, backend fetches data → writes summary to Firestore
Frontend reads directly from Firestore (super-fast + real-time)
Pub/Sub triggers to invalidate/update cache
Benefits:
50x faster reads
Real-time updates
Reduced load on Cloud SQL
Used in: e-commerce dashboards, admin panels, SaaS apps.
2️⃣ Firestore → Bigtable for Analytics / ML Pipelines
Firestore is great for app data, but not for analytics.
Workflow:
Firestore triggers Cloud Functions on data change
Data exported to Pub/Sub
Dataflow job writes to Bigtable
Bigtable acts as analytics-ready storage
ML models (Vertex AI) or Spark Dataproc read from Bigtable
Benefits:
Offload analytics from Firestore
Build real-time machine learning pipelines
Cheap scalable storage for huge datasets
3️⃣ Bigtable → Cloud SQL Materialized Views
Bigtable stores raw events; Cloud SQL stores summarized views.
Workflow:
IoT or clickstream data stored in Bigtable
Dataflow job aggregates hourly/daily
Writes summarized metrics to Cloud SQL (materialized view)
Application dashboards read aggregated values
Useful for:
✓ Analytics dashboards
✓ Billing systems
✓ Monitoring platforms
4️⃣ Multi-DB Workflow: Cloud SQL + Firestore + Bigtable
Many modern apps need all 3 databases:
Layer Database Purpose
Transactional Cloud SQL Payments, orders, accounts
Real-time app Firestore Chat, events, sessions
Bulk analytics Bigtable Historical logs, ML datasets
Workflow Example (E-commerce):
Orders saved in Cloud SQL
Cart + recommendations stored in Firestore
Historical clickstream stored in Bigtable
Analytics engine reads from Bigtable
Resulting prediction (e.g., recommended product) written back to Firestore
5️⃣ Firestore → Cloud SQL Data Normalization
Firestore is flexible but not suitable for relational data.
Workflow:
Firestore receives app/IoT/mobile events
Cloud Functions validate & normalize data
Insert into Cloud SQL with strict schema
Reporting (Looker Studio, Data Studio) runs SQL queries
Best for:
Systems needing real-time ingestion + relational reporting
Audit logs
Enterprise systems with strict compliance requirements
6️⃣ Bigtable as a Feature Store (Machine Learning)
Bigtable is commonly used as a feature store.
Workflow:
Process raw logs/events in Dataproc / Dataflow
Store features (user_id → {features…}) in Bigtable
Vertex AI batch/online predictions read from Bigtable
Prediction results are written back to Firestore for real-time UI updates
Used in:
✓ Fraud detection
✓ Recommendations
✓ Personalized search
✓ Customer segmentation
🧠 Key Integration Patterns
Pub/Sub + Dataflow:
The glue for moving data between databases.
Examples:
Firestore → Pub/Sub → Dataflow → Bigtable
Cloud SQL → Dataflow → Firestore
Bigtable → Dataflow → Cloud SQL
Cloud Functions:
Trigger per-document changes.
Examples:
Update Firestore when Cloud SQL row changes
Normalize Firestore data into Cloud SQL table
Dataproc + Bigtable:
Fast Spark jobs for:
ML preprocessing
Time-series analytics
Batch pipeline creation
🚀 Final Cheat Sheet
Database Best For Avoid When
Cloud SQL OLTP, transactions, complex queries Huge datasets (>2TB), IoT write-heavy workloads
Firestore Real-time apps, mobile sync Analytics, heavy aggregations
Bigtable Massive scale, time-series, ML Joins, SQL queries, small datasets
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