📌 ETL Pipeline Design Summary

ETL pipeline design is the process of planning and building a system that moves data from various sources to a destination, such as a data warehouse. ETL stands for Extract, Transform, Load, which are the three main steps in the process. The design involves deciding how data will be collected, cleaned, changed into the right format, and then stored for later use.

🙋🏻‍♂️ Explain ETL Pipeline Design Simply

Think of an ETL pipeline like a factory assembly line for data. Raw materials, or data, are collected from different places, cleaned up, and shaped into useful products before being stored in a warehouse. This way, the finished data is ready for anyone who needs to use it.

📅 How Can it be used?

You can use an ETL pipeline to automatically collect and prepare sales data from different shops for a central reporting dashboard.

🗺️ Real World Examples

A supermarket chain uses an ETL pipeline to gather daily sales data from hundreds of stores, standardise the formats, remove errors, and load the clean information into a central database where managers can analyse trends and performance.

A healthcare provider sets up an ETL pipeline to extract patient records from multiple clinics, convert them into a unified format, and load the information into a secure analytics system to track patient outcomes.

✅ FAQ

An ETL pipeline is a system that moves data from different sources into one place, like a data warehouse, so it can be used for reporting or analysis. It is important because it helps organisations collect, clean, and organise their data in a way that makes it useful for making decisions.

The main steps are extracting data from various sources, transforming it by cleaning and changing it into a usable format, and then loading it into a destination where it can be stored and accessed later. Good design makes sure each step works smoothly and the data stays accurate.

ETL pipeline design helps improve data quality by including steps to clean and standardise data before it is stored. This means errors are fixed, duplicates are removed, and the information is put into a consistent format, making it more reliable for anyone who needs to use it.

📚 Categories

• AI Infrastructure
• Data Engineering
• Data Governance

🔗 External Reference Links

ETL Pipeline Design link

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