7.1 Real-World ETL Examples Using Matillion
Retail Analytics
Scenario: A retail chain wants to analyze customer purchase behavior.
ETL Flow:
1. Extract: Pull sales data from POS systems, customer data from CRM, and product data from inventory DB.
2. Transform: Join sales with customer demographics, aggregate sales by product category and region, filter for top-selling products.
3. Load: Store results in Snowflake for dashboard reporting.
Healthcare Data Integration
Scenario: A hospital integrates patient records from multiple systems.
ETL Flow:
1. Extract: Pull data from EHR systems, lab results, and appointment scheduling tools.
2. Transform: Standardize patient IDs, clean missing values, join patient visits with diagnoses.
3. Load: Load into Redshift for clinical analytics and reporting.
Financial Fraud Detection
Scenario: A bank monitors transactions for fraud.
ETL Flow:
1. Extract: Pull transaction logs from multiple branches.
2. Transform: Filter for high-value transactions, apply anomaly detection using Python scripts, join with customer profiles.
3. Load: Store flagged transactions in a secure database for investigation
Supply Chain Optimization
Scenario: A manufacturing company wants to optimize inventory.
ETL Flow:
1. Extract: Pull data from warehouse systems, supplier APIs, and sales forecasts.
2. Transform: Calculate inventory turnover, predict stockouts using historical trends, aggregate supplier delivery times.
3. Load: Push insights into BigQuery for dashboard visualization.