👨‍💻 Here is our third week of Learning in Public with dlt — an open-source Python library for building real-world data pipelines! This time, we focused on a common and practical use case: loading local CSV files into DuckDB using dlt. Step by step, we explored how to build a robust pipeline with modular components and real-world readiness. 💡 This week’s highlights ✅ Load multiple CSVs easily with dlt.filesystem() ✅ Use @dlt.transformer for chunked processing of large files ✅ Apply smart incremental loading with modification_date ✅ Merge datasets with schema and primary key awareness into DuckDB ✨ Why it matters: Even though this example uses local files, the exact same pattern works for cloud sources like AWS S3, GCS, or remote file systems — just by tweaking the config. We’re learning tools that scale! 🔗 GitHub Repo – Week 3: https://github.com/1997mahadi/Data-Engineering-with-dlt/tree/main/week-03_filesystem-to-duckdb 🔗 LinkedIn Post: https://www.linkedin.com/posts/mahadi-nagassou-850a87254_dataengineering-python-dlt-activity-7364642175470493696-NmZN?utm_medium=ios_app&rcm=ACoAAD7Os_wBFAAF1g7fKTnQFhj7G6uWNYukvWw&utm_source=social_share_send&utm_campaign=copy_link If you’ve been meaning to build a practical understanding of data engineering — this series is for you. Let’s keep learning and shipping together! 🚀