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What is ETL?

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ETL stands for Extract, Transform, Load. It's a process used to move and prepare data:

1. Extract: Get data from different sources like databases, files, or APIs.
2. Transform: Clean and organize the data, like fixing errors, combining data, or changing formats.
3. Load: Save the prepared data into a system like a database or data warehouse, where it’s ready for analysis.

ETL helps make raw data usable and reliable for decision-making.

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Real-World Example of ETL

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Scenario: A company wants to analyze sales performance across its website, mobile app, and physical stores.

1. Extract:
   • Data is collected from different sources:
     • Website: Orders and customer interactions from the website database.
     • Mobile App: Transaction logs and user data.
     • Physical Stores: Sales data from a Point of Sale (POS) system.

1. Transform:
   • Fix errors: Remove duplicate or incorrect entries.
   • Standardize formats: Ensure dates, currencies, and product codes match across all data sources.
   • Enrich data: Combine data from multiple sources to create a unified sales record.

1. Load:
   • Store the cleaned and unified data in a data warehouse (e.g., Amazon Redshift, Google BigQuery).
   • This allows analysts to query the data and create reports to understand customer trends and improve sales strategies.