Note:
This article was originally started to write in Sept. 2024, probably for a learning purpose, but not finished yet till now, 03/09/2025. Today, this article will be completed by AI that makes the learning work much easier.
The following paragraph was originally written on 09/13/2024:
ETL stands [ref] for "extract, transform, and load" and is a process used to combine data from multiple sources into a single database or data warehouse. The three steps of ETL are:
- Extract: Data is taken from its original source, such as an application or database
- Transform: The data is cleaned, deduplicated, combined, and otherwise prepared for storage
- Load: The data is stored into the target database or data warehouse
ETL is a traditional way to prepare data for analytics, machine learning, and data science. It can make it faster and more efficient to run reporting and analytics workflows on enterprise data. ETL also helps to create an environment for data governance and data democracy.
ELT (extract, load, transform) is a related approach that pushes processing down to the database for better performance.
The following are obtained from AI today 03/09/2025.
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Walk through an example for building an ETL pipeline? 🚀
ChatGPT said:
Let’s walk through an example of an ETL process! Suppose you run an e-commerce store, and you want to analyze sales data. You have data in multiple places:
- Database: Order details (order ID, product, price, customer ID)
- CSV File: Customer information (customer ID, name, location)
- API: Shipping status
Here’s how ETL would work:
📥 Extract
You gather the data from all three sources:
- Query order details from the database
- Read the CSV file containing customer info
- Make API calls to get shipping status
🔧 Transform
Next, you clean and process the data:
- Join order and customer data using the customer ID
- Convert date formats for consistency
- Calculate new fields (like total revenue per order)
- Handle missing values (e.g., fill blanks with defaults)
📊 Load
Finally, you load the transformed data into a data warehouse (like Amazon Redshift or Google BigQuery):
- Save the processed data into a structured table
- Enable analytics tools to run reports and visualizations
Would you like me to guide you through building this with a tool like Python (using libraries like Pandas and SQLAlchemy) or an ETL platform like Apache Airflow? Let me know — we can even draft some code together! 🚀
Post Note:
This article was originally written by a traditional manner, and then is finished by AI. In fact, the manner of AI is just a
Tradional way to write an article:
- Find a topic or obtain an issue from somewhere
- Search the topic, mostly by Google, to collect the related info, probably get them as References
- Read the collected matirials, and understand and extract the basic points
- Make a written note to for the article
Now by AI:
The whole process above are completed by one Click or one group of Clicks to directly get the solution.
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