We have all been there – especially in the early stages of our careers.

A client or stakeholder asks for an urgent analysis.

You know you’ll need data from multiple departments, so you drop a mail to everyone at once.

• Department 1 replies the same day.
• Another department sends their file the next morning.
• A third one responds a few hours later.

You pull everything into Excel and start crunching numbers… only to realize something doesn’t add up. The datasets were captured at different points in time.

Now you’re dealing with mismatched numbers, endless reconciliations, and growing frustration.

👉 Wouldn’t it have been easier if all that data had been captured and combined at the same time?

This is where ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) step in as lifesavers.

These approaches automate the movement and preparation of data, ensuring consistency, scalability, and reliability — so you can focus on analysis instead of chasing files.

📌 What is ETL?

ETL = Extract → Transform → Load

1. Extract – Fetch data from different sources:
   • Websites
   • Event trackers (Mixpanel, Clevertap)
   • CSV files
   • APIs (Twitter, YouTube)
   • Traditional databases
2. Transform – Clean and shape the data:
   • Remove duplicates
   • Filter unnecessary fields
   • Aggregate values
   • Mask PII (personally identifiable information)
   • Create derived columns
3. Load – Push the transformed data into a data warehouse for BI/reporting.