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Transferring Data with ETL

Data Extraction Methods Overview

Explore various data extraction methods critical for ETL processes. Learn to use SQL for querying databases, Python for web scraping and API access, and libraries like pandas for extracting data from files. Understand how to connect to databases with Python drivers for effective data retrieval.

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There are several methods for extracting data, depending on the type of data, the source of the data, and the purpose of the extraction. This lesson intends to provide a brief overview of some of the most popular ones.

SQL queries

The most common method of extracting data is via SQL. SQL queries extract structured data from relational databases and data warehouses. SQL is a powerful tool that can be used to solve various tasks, from simple data retrieval to complex data analysis and management.

Basic SQL syntax is easy to learn, and structured data is easy to process, making SQL a very common method for extracting data. After querying the required data from the database, we can use SQL to export the query as a CSV file.

  • We use a select statement to choose the relevant columns to retrieve data from a database.

  • We then add from to choose the table.

  • To filter the retrieved data, we use the where clause.

  • We group rows using group by and then aggregate them using functions like count, min, max, sum, average, and more.

  • Finally, we use a copy ...