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Apache Airflow: A Comprehensive Guide to Workflow Orchestration

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apache airflow

An Apache Airflow training helps you master the open-source workflow orchestration platform. Explore the reasons and methods to become proficient in this essential tool for Data Scientists, Data Engineers, and Machine Learning Engineers.

Apache Airflow is a workflow engine and orchestration tool that allows you to schedule and execute complex data pipelines.

With this open-source tool, you can be confident that each task in the pipeline will be executed in the correct order and has the necessary resources. This platform is a must-have for Data Engineering, Data Science, and Machine Learning.

Why learn to use Apache Airflow?

Automation has become a critical focus in all industries, enabling companies to increase their productivity and competitiveness.

It’s essential to automate as many tasks as possible to avoid manually repeating the same procedures. For example, in engineering, data science, and the field of Machine Learning, collecting data from multiple databases can be automated using Apache Airflow.

In general, this tool allows for the management and automation of the data ETL (extraction, transformation, loading) process at the core of Data Engineering and Data Science tasks. Similarly, Airflow enables the scheduling and automation of Machine Learning pipelines.

Airflow is ideal for orchestrating Data Science and Machine Learning workflows, thanks to its numerous monitoring, sensing, and customization features. Additionally, this solution is integrated with major Big Data services like Hadoop and Spark.

It’s also possible to use Airflow in combination with the Docker containerization platform for deploying Data Science workflows. Docker simplifies the creation, deployment, and execution of applications through containers, which package an application and all its dependencies. Meanwhile, Airflow automates the Data Scientist’s workflow and monitors pipelines in production.

How to follow an Apache Airflow training?

To learn how to use Apache Airflow, you can turn to DataScientest’s training courses. This tool is among those you will learn to use through the “automation and deployment” module in our Data Engineer training, alongside Docker and Flask.

This comprehensive training program offers you the opportunity to learn the profession of Data Engineer. You will discover all the intricacies of ETL processes, deploying Machine Learning models to production, and creating data processing pipelines for streaming data.

Apache Airflow is also part of our Data Scientist curriculum. This path allows you to learn the profession of Data Scientist. You will learn how to select the right data to solve a company’s data challenges, model data analysis results, and develop Machine Learning pipelines.

Both of these training programs are available in BootCamp format or as continuous education and combine in-person and online learning through an innovative Blended Learning approach in France. Both pathways allow you to earn a diploma certified by the University of Sorbonne.

If you are already a Data Scientist and want to enhance your skills by learning how to deploy Machine Learning models, we also offer a Machine Learning Engineer training program. Once again, you can learn how to use Airflow through the dedicated automation module in this program.

You now know why and how to pursue an Airflow training. Explore other tools in Data Engineering, such as Snowflake’s Data Warehouse Cloud or the code hosting service GitHub.


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