Ready to level up your pipelines and build like a pro?
In this course, you'll go far beyond the basics. You’ll build custom sources, integrate with complex APIs and databases, control how your data lands in warehouses or files, transform it, and deploy pipelines in real-world production environments like Airflow or Lambda.
Master the internals of
dlt, learn best practices, and confidently build modern, production-grade data pipelines.
Learn how to build flexible REST API connectors from scratch using @dlt.resource and the powerful RESTClient.
Connect to any SQL-compatible database, reflect table schemas, write query adapters, and selectively ingest data using sql_database.
Build sources that read from local or remote files (S3, GCS, Azure).
Use @dlt.destination to send data back to APIs like Notion, Slack, or Airtable. Learn batching, retries, and idempotent patterns.
Learn when and how to apply add_map, add_filter, @dlt.transformer, or even post-load transformations via SQL or Ibis. Control exactly how your data looks.
Understand how to use replace and merge, and combine them with schema hints and incremental loading.
Define expectations on schema, enforce data types and behaviors, and lock down your schema evolution. Ensure reliable downstream use of your data.
Track every step of your pipeline: from extraction to load. Use logs, traces, and metadata to debug and analyze performance.
Handle large datasets, tune buffer sizes, parallelize resource extraction, optimize memory usage, and reduce pipeline runtime.
Bring your pipelines into production. Learn how to run dlt on AWS Lambda, GitHub Actions, DAGs in Airflow, or within Dagster projects.
You’ve finished the dlt Advanced Course — well done!
Test your skills with the Advanced Certification Homework: https://dlthub.learnworlds.com/course/dlt-advanced
Need help or stuck on something?
Join the Community and ask away in #dlthub-education channel — we’re happy to help.