ETL dan ELT di Python

Dive into leveraging pandas to extract, transform, and load data as you build your first data pipelines. Learn how to make your ETL logic reusable, and apply logging and exception handling to your pipelines.

Supercharge your workflow with advanced data pipelining techniques, such as working with non-tabular data and persisting DataFrames to SQL databases. Discover tooling to tackle advanced transformations with pandas, and uncover best-practices for working with complex data.

In this final chapter, you’ll create frameworks to validate and test data pipelines before shipping them into production. After you’ve tested your pipeline, you’ll explore techniques to run your data pipeline end-to-end, all while allowing for visibility into pipeline performance.