The Complete Hands-On Introduction to Apache Airflow
4.4 (1,721 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
8,826 students enrolled

The Complete Hands-On Introduction to Apache Airflow

Learn to author, schedule and monitor data pipelines through practical examples using Apache Airflow
4.4 (1,721 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
8,826 students enrolled
Created by Marc Lamberti
Last updated 7/2020
English
English [Auto], French [Auto], 1 more
  • Portuguese [Auto]
Current price: $34.99 Original price: $49.99 Discount: 30% off
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This course includes
  • 5 hours on-demand video
  • 9 articles
  • 9 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Create plugins to add functionalities to Apache Airflow.
  • Using Docker with Airflow and different executors
  • Master core functionalities such as DAGs, Operators, Tasks, Workflows, etc
  • Understand and apply advanced concepts of Apache Airflow such as XCOMs, Branching and SubDAGs.
  • The difference between Sequential, Local and Celery Executors, how do they work and how can you use them.
  • Use Apache Airflow in a Big Data ecosystem with Hive, PostgreSQL, Elasticsearch etc.
  • Install and configure Apache Airflow
  • Think, answer and implement solutions using Airflow to real data processing problems
Course content
Expand all 70 lectures 05:02:10
+ First Approach to Airflow
8 lectures 38:40
Why use Airflow?
01:45
How Airflow works?
04:07
[Practice] Installing Airflow
10:11
[Practice] Quick Tour of Airflow UI
08:55
[Practice] Quick Tour of Airflow CLI
05:28
Quiz Time!
6 questions
Recap
03:23
+ Coding Your First Data Pipeline with Airflow
16 lectures 01:11:47
Introduction
00:31
[Practice] Time to code your first DAG
01:45
[Practice] DAG Skeleton
05:17

- Operators

- Sensors

- Transfer

What is an Operator?
03:13
[Practice] Checking if tweets are available - FileSensor
13:33
[Practice] Fetching and cleaning tweets - PythonOperator
07:53
[Practice] Storing tweets into HDFS - BashOperator
04:13
[Practice] Loading tweets into Hive - HiveOperator
08:41
Operator Relationships and Bitshift Composition
01:43
[Practice] Adding dependencies
03:19
[Practice] The Twitter DAG in action!
04:21
How the Scheduler Works?
03:55
[Practice] A Quick Play With Backfill and Catchup
06:38
What is a Workflow?
00:43
Quiz Time!
7 questions
Recap
02:31
+ Databases and Executors
7 lectures 42:33
Introduction
00:52
Local Executor with MySQL
04:23
[Practice] Configuring your DAG with the Local Executor and MySQL
11:28
Celery Executor with MySQL and RabbitMQ
05:56
[Practice] Configuring your DAG with Celery Executor, MySQL and RabbitMQ
09:51
Quiz Time!
5 questions
Recap
02:32
+ Implementing Advanced Concepts in Airflow
12 lectures 57:45
Introduction
00:34
Minimising Repetitive Patterns With SubDAGs
01:39
[Practice] Minimising a DAG with SubDAGs
12:07
How to Interact With External Sources Using Hooks
01:29
[Practice] Getting data from MySQL using Hooks
05:56
How to Share Data Between Your Tasks With XCOMs
02:44
[Practice] Sharing data between tasks with XComs
08:42
How to Execute Tasks According To Criteria Using Branching
03:25
[Practice] Executing a task according to a condition
06:13
Control Your Tasks With SLAs
05:42
[Practice] Defining SLAs in your DAGs
05:57
Quiz Time!
5 questions
Recap
03:17
+ Creating Airflow Plugins with Elasticsearch and PostgreSQL
6 lectures 33:16
Introduction
00:31
Adding Functionalities to Apache Airflow
03:54
[Practice] Creating a hook to interact with Elasticsearch
07:40
[Practice] Creating a transfer operator to move data from MySQL to Elasticsearch
09:47
[Practice] Adding a new view to the Airflow UI
10:10
Quiz Time!
5 questions
Recap
01:14
+ Using Apache Airflow with Docker
7 lectures 32:10
Introduction
00:47
Quick Reminder About Docker
03:17
[Practice] Building and running your first Airflow Docker image
07:07
[Practice] Running Airflow using Sequential Executor with Docker
08:24
[Practice] Running Airflow using Local Executor with Docker
04:24
[Practice] Scaling Airflow using Celery Executor with Docker
06:20
Quiz Time!
5 questions
Recap
01:51
+ Airflow 2.0
1 lecture 10:41
What to expect from Airflow 2.0?
10:41
+ BONUS - APPENDIX
8 lectures 03:26
[BLOG POST] How to use the DockerOperator with Templating and Apache Spark
00:31
[BLOG POST] Apache Airflow with Kubernetes Executor
00:31
[BLOG POST] How to use templates and macros in Apache Airflow
00:41
[BLOG POST] How to use timezones in Apache Airflow
00:40
[BLOG POST] How to use the BashOperator
00:13
[BLOG POST] Variables in Apache Airflow: The Guide
00:14
[VIDEO] Running Apache Airflow on a multi-nodes Kubernetes cluster locally
00:21
COUPON FOR MY OTHER COURSES!
00:12
Requirements
  • VirtualBox must be installed - A VM of 3Gb will have to be downloaded
  • At least 8 gigabytes of memory
  • Some prior programming or scripting experience. Python experience will help you a lot but since it's a very easy language to learn, it shouldn't be too difficult if you are not familiar with.
Description

Apache Airflow is an open-source  platform to programmatically author, schedule and monitor workflows. If you have many ETL(s) to manage, Airflow is a must-have.

In this course you are going to learn everything you need to start using Apache Airflow through theory and pratical videos. Starting from very basic notions such as, what is Airflow and how it works, we will dive into advanced concepts such as, how to create plugins and make real dynamic pipelines.

Who this course is for:
  • People being curious about data engineering.
  • People who want to learn basic and advanced concepts about Apache Airflow.
  • People who like hands-on approach.