3.ETL Concepts in Action
ETL (Extract, Transform, Load) is the process of moving and preparing data for analysis.
1. Extract – Gathering Data
Pull raw data from multiple sources such as:
🡆Databases (PostgreSQL, MySQL)
🡆SaaS platforms (Salesforce, Shopify)
🡆Flat files (CSV, Excel)
The data is staged temporarily before processing.
Example:
A retailer extracts daily sales from POS systems, customer data from CRM, and inventory data from a warehouse database.
2. Transform – Cleaning & Structuring Data
Data is standardized, cleaned, and enriched. Common transformations include:
🡆Filtering: Keep only relevant rows (e.g., transactions in the last 30 days)
🡆Joining: Merge datasets (e.g., match customer IDs between CRM and POS data)
🡆Aggregating: Summarize data (e.g., total daily sales per store)
🡆Cleaning: Fix typos, correct missing fields, ensure consistent naming
🡆Deduplication: Remove repeated entries
Example:Combine “John Smith” and “J. Smith” entries under one customer ID, ensuring consistency across systems.
3. Load – Delivering Data
The cleaned and structured data is loaded into a target system like a data warehouse or data lake for analytics.
🡆Example:Load the final dataset into Snowflake, where analysts can query it using BI tools such as Power BI or Tableau to identify sales trends or customer churn rates.
🡆Matillion supports batch and real-time streaming ETL, allowing data to stay fresh and analysis-ready.