Apache Airflow: The Operators Guide
What you'll learn
- Master Apache Airflow Operators
- How to version your DAGs
- How to create DAG dependencies efficiently
- How to trigger your DAGs on specific days
- Create Incredible Data Pipelines by truly understanding Airflow operators
- Get used to Airflow, you ALREADY know the basics of Airflow
- Docker and Docker compose installed
Apache Airflow has more than 700 Operators and 70 tools it can interact with.
It's huge! ?
Operators are tasks in your data pipeline. They are important as they correspond to the actions of your data pipeline, to the different steps to produce the output you want.
Do you know well the Airflow's operators?
Do you want to create reliable data pipelines?
Are you looking for best practices around Operators?
If yes, you've come to the right place!
With the course Apache Airflow: The Operators Guide, will be able to
Version your DAGs
Retry your tasks properly
Create dependencies between your tasks and even your DAG Runs
Demystifies the owner parameter
Take actions if a task fails
Choose the right way to create DAG dependencies
Execute a task only in a specific interval of time
Group your tasks to make your DAG cleaner (not with SubDAG)
Trigger your DAG based on a Calendar
and much more!
Warning! You must already know Airflow! Think of this course as your Airflow Operators Reference.
The operator you are looking for is there?
Vote for it, and I will make video in the month. You decide!
If you already know Airflow and you're ready to step up! Enroll now and truly take your data pipelines to another level. ?
Who this course is for:
- Data engineers
- Data scientist
- Business Analysts
- Software Engineers
My name is Marc Lamberti, I'm 27 years old and I'm very happy to arouse your curiosity! I'm currently working as Big Data Engineer in full-time for the biggest online bank in France, dealing with more than 1 500 000 clients. For more than 3 years now, I created different ETLs in order to address the problems that a bank encounters everyday such as, a platform to monitor the information system in real time to detect anomalies and reduce the number of client's calls, a tool detecting in real time any suspicious transaction or potential fraudster, an ETL to valorize massive amount of data into Cassandra and so on.
The biggest issue when you are a Big Data Engineer is to deal with the growing number of available open source tools. You have to know how to use them, when to use them and how they connect to each other in order to build robust, secure and performing systems solving your underlying business needs.
I strongly believe that the best way to learn and understand a new skill is by taking a hands-on approach with just enough theory to explain the concepts and a big dose of practice to be ready in a production environment. That's why in each of my courses you will always find practical examples associated with theoric explanations.
Have a great learning time!