Oreilly – Data Pipelines with Apache Airflow, video edition 2022-12

Oreilly – Data Pipelines with Apache Airflow, video edition 2022-12

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Apr 18, 2025 - 18:31
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Oreilly – Data Pipelines with Apache Airflow, video edition 2022-12
Oreilly – Data Pipelines with Apache Airflow, video edition 2022-12

Data Pipelines with Apache Airflow video edition. This course is a video version of the best-selling book. In this edition, the narrator reads the book aloud while the content, charts, code, and text from the book are displayed on the screen. The experience is similar to listening to an audiobook, except that you can also see the content visually. Data pipelines manage the flow of data from initial collection to integration, cleansing, analysis, visualization, and more. Apache Airflow is a single platform that you can use to design, implement, monitor, and maintain your pipeline. The easy-to-use interface, out-of-the-box options, and flexible scriptability with Python make Airflow ideal for any data management task.

This book teaches you how to build and maintain an effective data pipeline. You’ll explore common usage patterns, including gathering data from multiple sources, connecting to data lakes, and deploying to the cloud. A combination of reference and training, this practical guide covers all aspects of the directed acyclic graphs (DAGs) that power Airflow and shows you how to customize them to meet your pipeline needs.

What you will learn:

  • Build, test, and deploy Airflow pipelines as DAGs
  • Automate data movement and transformation
  • Analyzing historical datasets using backfilling
  • Custom component development
  • Airflow setup in production environments

This course is suitable for people who:

  • They work in the fields of DevOps, data engineering, machine learning engineering, and administrative systems.
  • Have intermediate skills in Python programming.

Data Pipelines with Apache Airflow video edition course details

Course headings

  1. Part 1. Getting started
  2. Chapter 1 Meet Apache Airflow
  3. Chapter 1 Pipeline graphs vs. sequential scripts
  4. Chapter 1 Introducing Airflow
  5. Chapter 1 When to use Airflow
  6. Chapter 2 Anatomy of an Airflow DAG
  7. Chapter 2 Running a DAG in Airflow
  8. Chapter 2 Running at regular intervals
  9. Chapter 3 Scheduling in Airflow
  10. Chapter 3 Cron-based intervals
  11. Chapter 3 Processing data incrementally
  12. Chapter 3 Understanding Airflow’s execution dates
  13. Chapter 3 Best practices for designing tasks
  14. Chapter 4 Templating tasks using the Airflow context
  15. Chapter 4 Templating the PythonOperator
  16. Chapter 4 Hooking up other systems
  17. Chapter 5 Defining dependencies between tasks
  18. Chapter 5 Branching
  19. Chapter 5 Conditional tasks
  20. Chapter 5 More about trigger rules
  21. Chapter 5 Sharing data between tasks
  22. Chapter 5 Chaining Python tasks with the Taskflow API
  23. Part 2. Beyond the basics
  24. Chapter 6 Triggering workflows
  25. Chapter 6 Polling custom conditions
  26. Chapter 6 Triggering other DAGs
  27. Chapter 7 Communicating with external systems
  28. Chapter 7 Developing locally with external systems
  29. Chapter 7 Moving data from between systems
  30. Chapter 8 Building custom components
  31. Chapter 8 Building a custom hook
  32. Chapter 8 Building a custom operator
  33. Chapter 8 Packaging your components
  34. Chapter 9 Testing
  35. Chapter 9 Setting up a CI/CD pipeline
  36. Chapter 9 Testing with files on disk
  37. Chapter 9 Working with external systems
  38. Chapter 9 Using tests for development
  39. Chapter 10 Running tasks in containers
  40. Chapter 10 Introducing containers
  41. Chapter 10 Containers and Airflow
  42. Chapter 10 Creating container images for tasks
  43. Chapter 10 Running tasks in Kubernetes
  44. Chapter 10 Using the KubernetesPodOperator
  45. Part 3. Airflow in practice
  46. Chapter 11 Best practices
  47. Chapter 11 Manage credentials centrally
  48. Chapter 11 Use factories to generate common patterns
  49. Chapter 11 Designing reproducible tasks
  50. Chapter 11 Handling data efficiently
  51. Chapter 11 Managing your resources
  52. Chapter 12 Operating Airflow in production
  53. Chapter 12 Which executor is right for me?
  54. Chapter 12 A closer look at the scheduler
  55. Chapter 12 Installing each executor
  56. Chapter 12 Setting up the KubernetesExecutor
  57. Chapter 12 Capturing logs of all Airflow processes
  58. Chapter 12 Visualizing and monitoring Airflow metrics
  59. Chapter 12 Creating dashboards with Grafana
  60. Chapter 12 How to get notified of a failing task
  61. Chapter 12 Scalability and performance
  62. Chapter 13 Securing Airflow
  63. Chapter 13 Encrypting data at rest
  64. Chapter 13 Encrypting traffic to the webserver
  65. Chapter 13 Fetching credentials from secret management systems
  66. Chapter 14 Project: Finding the fastest way to get around NYC
  67. Chapter 14 Extracting the data
  68. Chapter 14 Structuring a data pipeline
  69. Part 4. In the clouds
  70. Chapter 15 Airflow in the clouds
  71. Chapter 15 Google Cloud Composer
  72. Chapter 16 Airflow on AWS
  73. Chapter 16 AWS-specific hooks and operators
  74. Chapter 16 Building the DAG
  75. Chapter 17 Airflow on Azure
  76. Chapter 17 Overview
  77. Chapter 18 Airflow in GCP
  78. Chapter 18 Integrating with Google services
  79. Chapter 18 GCP-specific hooks and operators
  80. Chapter 18 Getting data into BigQuery

Course images

Data Pipelines with Apache Airflow video edition

Sample course video

Installation Guide

After Extract, view with your favorite player.

Subtitles: None

Quality: 720p

Download link

Download Part 1 – 1 GB

Download Part 2 – 303 MB

File(s) password: www.downloadly.ir

File size

1.3 GB

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