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commit f54744fb76f0739dc6695b373cda241ec2e23156 Author: Didier Durand <[email protected]> AuthorDate: Mon Nov 24 16:02:41 2025 +0100 [Doc] fixing 404 error for incorrect ETL link (#1265) * [Doc] fixing 404 error for incorrect ETL link * [Doc] Removed the link to astronomer site --- landing-pages/site/content/en/use-cases/etl_analytics.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/landing-pages/site/content/en/use-cases/etl_analytics.md b/landing-pages/site/content/en/use-cases/etl_analytics.md index c578c3e22c..ea94f2a53a 100644 --- a/landing-pages/site/content/en/use-cases/etl_analytics.md +++ b/landing-pages/site/content/en/use-cases/etl_analytics.md @@ -13,9 +13,9 @@ blocktype: use-case </div> -Extract-Transform-Load (ETL) and Extract-Load-Transform (ELT) data pipelines are the most common use case for Apache Airflow. 90% of respondents in the 2023 Apache Airflow survey are using Airflow for ETL/ELT to power analytics use cases. +Extract-Transform-Load (ETL) and Extract-Load-Transform (ELT) data pipelines are the most common use case for Apache Airflow. 90% of respondents in the 2023 Apache Airflow survey are using Airflow for ETL/ELT to power analytics use cases. -The video below shows a simple ETL/ELT pipeline in Airflow that extracts climate data from a CSV file, as well as weather data from an API, runs transformations and then loads the results into a database to power a dashboard. You can find the code for this example [here](https://github.com/astronomer/airflow-quickstart). +The video below shows a simple ETL/ELT pipeline in Airflow that extracts climate data from a CSV file, as well as weather data from an API, runs transformations and then loads the results into a database to power a dashboard. <div id="videoContainer" style="display: flex; justify-content: center; align-items: center; border: 2px solid #ccc; width: 75%; margin: auto; padding: 20px;"> @@ -33,7 +33,7 @@ Airflow is the de-facto standard for defining ETL/ELT pipelines as Python code. - **Tool agnostic**: Airflow can be used to orchestrate ETL/ELT pipelines for any data source or destination. - **Extensible**: There are many Airflow modules available to connect to any data source or destination, and you can write your own custom operators and hooks for specific use cases. - **Dynamic**: In Airflow you can define [dynamic tasks](https://airflow.apache.org/docs/apache-airflow/stable/authoring-and-scheduling/dynamic-task-mapping.html), which serve as placeholders to adapt at runtime based on changing input. -- **Scalable**: Airflow can be scaled to handle infinite numbers of tasks and workflows, given enough computing power. +- **Scalable**: Airflow can be scaled to handle infinite numbers of tasks and workflows, given enough computing power. ## Airflow features for ETL/ELT pipelines
