dlt Use Cases
API to Data Warehouse: GitHub Analytics
Problem
Track GitHub repository activity (issues, pull requests, commits) in BigQuery for analysis and dashboarding.
Solution
Use dlt to incrementally load GitHub data to BigQuery.
Implementation:
Configuration (.dlt/secrets.toml):
Schedule with Airflow:
Impact:
- Automated GitHub data collection
- Incremental updates (only new/updated data)
- Ready for dashboarding in Looker/Tableau
- Historical tracking of repository activity
Database Replication: PostgreSQL to Snowflake
Problem
Replicate production PostgreSQL database to Snowflake for analytics without impacting production.
Solution
Use dlt to incrementally replicate tables from PostgreSQL to Snowflake.
Implementation:
Advanced: Custom Filtering:
Schedule for Daily Sync:
Impact:
- Zero impact on production database (read-only replica)
- Near real-time analytics (hourly/daily updates)
- Historical data preservation
- Reduced Snowflake costs (incremental only)
SaaS Data Integration: Stripe to BigQuery
Problem
Centralize Stripe payment data in BigQuery for financial reporting and customer analytics.
Solution
Use dlt's verified Stripe source to load payment data incrementally.
Setup:
Configuration (.dlt/secrets.toml):
Implementation (stripe_pipeline.py):
Custom Transformations:
dbt Transformations (after loading):
Impact:
- Unified financial data in BigQuery
- Automated daily updates
- Ready for BI tools (Looker, Tableau)
- Combined with other business data
File Processing: CSV/Parquet to Warehouse
Problem
Load daily CSV exports from an SFTP server into Snowflake for analysis.
Solution
Use dlt to process files and load to warehouse with schema inference.
Implementation:
With SFTP:
Impact:
- Automated file processing
- Schema inference (no manual DDL)
- Incremental loading (track processed files)
- Error handling for malformed files
Real-Time Data: Webhook Events to Warehouse
Problem
Store webhook events from payment processor for audit and analytics.
Solution
Capture webhooks with FastAPI, load to warehouse with dlt.
Webhook Receiver (webhook_app.py):
Background Loader (loader.py):
Run Both:
Impact:
- Real-time event capture
- Batched warehouse loading (cost-effective)
- No lost events (queue buffer)
- Queryable event history
Multi-Source Aggregation: Marketing Data
Problem
Combine marketing data from Google Ads, Facebook Ads, and LinkedIn Ads into single dataset.
Solution
Create unified marketing dashboard with data from multiple ad platforms.
Implementation:
dbt Transformation (calculate ROAS):
Impact:
- Unified view across ad platforms
- Cross-platform attribution
- Consistent metrics and reporting
- Single source of truth for marketing data
Change Data Capture: Database Audit Log
Problem
Track all changes to critical tables (customers, orders) for audit and compliance.
Solution
Use dlt to capture and store database change events.
Implementation with Database Triggers:
dlt Pipeline:
Query Audit Trail:
Impact:
- Complete audit trail
- Compliance with regulations (GDPR, SOX)
- Debugging data issues
- Historical data reconstruction
Ready to implement these patterns? Check out Tutorials for step-by-step guides!