Data Engineering with Python and AI/LLMs - Data Loading Tutorial

Data Engineering with Python and AI/LLMs - Data Loading Tutorial

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1:10:42 5. How to configure secrets

13 of 33

13 of 33

1:10:42 5. How to configure secrets

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Data Engineering with Python and AI/LLMs - Data Loading Tutorial

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  1. 1 0:00:00 1. Introduction
  2. 2 0:08:02 2. What is data ingestion
  3. 3 0:10:04 3. Extracting data: Data Streaming & Batching
  4. 4 0:14:00 4. Extracting data: Working with RestAPI
  5. 5 0:29:36 5. Normalizing data
  6. 6 0:43:41 6. Loading data into DuckDB
  7. 7 0:48:39 7. Dynamic schema management
  8. 8 0:56:26 8. What is next?
  9. 9 0:56:36 1. Introduction
  10. 10 0:59:29 2. Overview
  11. 11 1:02:08 3. Extracting data with dlt: dlt RestAPI Client
  12. 12 1:08:05 4. dlt Resources
  13. 13 1:10:42 5. How to configure secrets
  14. 14 1:15:12 6. Normalizing data with dlt
  15. 15 1:24:09 7. Data Contracts
  16. 16 1:31:05 8. Alerting schema changes
  17. 17 1:33:56 9. Loading data with dlt
  18. 18 1:33:56 10. Write dispositions
  19. 19 1:37:34 11. Incremental loading
  20. 20 1:43:46 12. Loading data from SQL database to SQL database
  21. 21 1:47:46 13. Backfilling
  22. 22 1:50:42 14. SCD2
  23. 23 1:54:29 15. Performance tuning
  24. 24 2:03:12 16. Loading data to Data Lakes & Lakehouses & Catalogs
  25. 25 2:12:17 17. Loading data to Warehouses/MPPs,Staging
  26. 26 2:18:15 18. Deployment & orchestration
  27. 27 2:18:15 19. Deployment with Git Actions
  28. 28 2:29:04 20. Deployment with Crontab
  29. 29 2:40:05 21. Deployment with Dagster
  30. 30 2:49:47 22. Deployment with Airflow
  31. 31 3:07:00 23. Create pipelines with LLMs: Understanding the challenge
  32. 32 3:10:35 24. Create pipelines with LLMs: Creating prompts and LLM friendly documentation
  33. 33 3:31:38 25. Create pipelines with LLMs: Demo

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