8 days ago [This post was last modified: 8 days ago by Sauron ]
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Data Pipeline Automation and Orchestration with Python course. This course teaches techniques for automating and managing data pipelines using Python and tools such as Apache Airflow, Prefect, and Dagster. In today’s data engineering, automating and orchestrating pipelines is essential to increase reliability and efficiency. This course introduces participants to various tools by introducing the concepts of orchestration and its role in data pipelines. First, the importance of orchestration in pipelines is examined, then how to use Apache Airflow to automate data workflows is taught. Next, participants learn how to create lightweight and efficient pipelines with Prefect. This course provides learners with the skills necessary to design, automate, and manage data pipelines with Python.
What you will learn
  • Organizing Workflows in Data Engineering: Participants in this section will be introduced to the concept of organization, its importance, and its role in managing data pipelines.
  • Automating Data Workflows with Apache Airflow: Learn how to use the powerful Apache Airflow tool to define, schedule, and monitor complex data workflows.
  • Prefect for Pipeline-Style Automation: Learn how to use Prefect, a modern tool for organizing and automating data pipelines more easily and flexibly.
  • Monitoring and Maintaining Automated Data Workflows: After automation, participants will gain the skills necessary to monitor the performance and maintain automated workflows to ensure their proper and sustainable operation.
This course is suitable for people who:
  • Data Engineers: Those who seek to improve and optimize their data pipelines through automation.
  • Python developers: People who want to use Python to manage and organize data workflows.
  • Data Analysts: Those who want to automate data extraction, transformation, and loading (ETL) processes.
  • Anyone looking to learn how to organize data pipelines and related tools.

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