Watch Data Engineering with Python and AI/LLMs – Data Loading Tutorial Video Tutorial


Tutorial Details & Info

Tutorial Title: Data Engineering with Python and AI/LLMs – Data Loading Tutorial
Instructor / Channel: freeCodeCamp.org
Lesson Runtime: 48:41 Minutes
Publish Date: April 17, 2025
Total Students / Views: 164,706 views

Follow step-by-step with Data Engineering with Python and AI/LLMs – Data Loading Tutorial instructor freeCodeCamp.org. This full video course has a total duration of 48:41 minutes providing step-by-step visual instructions. Follow along to build your skills without any subscription or sign-up required.

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Course Description & Lesson Notes

Official Video Description:

Master data ingestion for data engineering with Python. Learn to tackle common pipeline failures like schema changes and API limits by adopting the mindset and practices of a senior platform engineer. This course covers essential techniques including extracting data from APIs, automatic schema management, incremental loading, and orchestrating scalable, automated workflows using modern tools. Course developed by Alexey Grigorev & Adrian Brudaru. 💻 Code: https://github.com/dlt-hub/dlthub-education/tree/main/courses/freecodecamp/de_with_dlt_2025 🏗️ dlthub.com provided a grant to make this course possible. ⭐️ Contents ⭐️ Alexey's part 0:00:00 1. Introduction 0:08:02 2. What is data ingestion 0:10:04 3. Extracting data: Data Streaming & Batching 0:14:00 4. Extracting data: Working with RestAPI 0:29:36 5. Normalizing data 0:43:41 6. Loading data into DuckDB 0:48:39 7. Dynamic schema management 0:56:26 8. What is next? Adrian's part 0:56:36 1. Introduction 0:59:29 2. Overview 1:02:08 3. Extracting data with dlt: dlt RestAPI Client 1:08:05 4. dlt Resources 1:10:42 5. How to configure secrets 1:15:12 6. Normalizing data with dlt 1:24:09 7. Data Contracts 1:31:05 8. Alerting schema changes 1:33:56 9. Loading data with dlt 1:33:56 10. Write dispositions 1:37:34 11. Incremental loading 1:43:46 12. Loading data from SQL database to SQL database 1:47:46 13. Backfilling 1:50:42 14. SCD2 1:54:29 15. Performance tuning 2:03:12 16. Loading data to Data Lakes & Lakehouses & Catalogs 2:12:17 17. Loading data to Warehouses/MPPs,Staging 2:18:15 18. Deployment & orchestration 2:18:15 19. Deployment with Git Actions 2:29:04 20. Deployment with Crontab 2:40:05 21. Deployment with Dagster 2:49:47 22. Deployment with Airflow 3:07:00 23. Create pipelines with LLMs: Understanding the challenge 3:10:35 24. Create pipelines with LLMs: Creating prompts and LLM friendly documentation 3:31:38 25. Create pipelines with LLMs: Demo 🎉 Thanks to our Champion and Sponsor supporters: 👾 Drake Milly 👾 Ulises Moralez 👾 Goddard Tan 👾 David MG 👾 Matthew Springman 👾 Claudio 👾 Oscar R. 👾 jedi-or-sith 👾 Nattira Maneerat 👾 Justin Hual -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

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🎓 Lesson Overview & Learning Outcomes:

Welcome to the step-by-step video guide for Data Engineering with Python and AI/LLMs – Data Loading Tutorial taught by freeCodeCamp.org. This tutorial provides a comprehensive walkthrough designed to take you from foundational principles to practical implementation.

💡 Key Topics Covered in This Course:

  • Core Fundamentals & Setup: Understanding the workspace, essential tools, and initial setup for Data Engineering with Python and AI/LLMs – Data Loading Tutorial.
  • Step-by-Step Practical Demonstration: Hands-on implementation guided by freeCodeCamp.org with real-world examples.
  • Best Practices & Key Shortcuts: Time-saving workflows, keyboard shortcuts, and industry-standard recommendations.
  • Troubleshooting & Common Pitfalls: How to avoid common beginner errors and optimize your workflow for peak efficiency.

📋 Recommended Prerequisites & Study Notes:

No prior advanced experience is required. Follow along with the video player above on any desktop computer, tablet, or mobile device. Pause and rewind at key steps to practice along with the instructor.

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