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  ETL stands for Extract, Transform, and Load . It is a process of extracting data from one or more sources, transforming it into a format that is more useful, and loading it into a data warehouse or data lake. In Python, ETL can be implemented using a variety of libraries and tools. Some popular options include: Pandas: Pandas is a powerful library for data manipulation and analysis. It can be used to extract data from a variety of sources, including CSV files, JSON files, and databases. PySpark: PySpark is a Python library for Apache Spark. Spark is a powerful distributed computing framework that can be used to process large datasets. SQLAlchemy: SQLAlchemy is a library for interacting with databases. It can be used to extract data from databases and load it into data warehouses or data lakes. Here is an example of how ETL can be used in machine learning. Let's say you want to build a machine learning model to predict the price of houses. You would first need to extract th...