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The following commit(s) were added to refs/heads/main by this push:
new d4468d481f [Doc] fixing 404 error for incorrect ETL link (#1265)
d4468d481f is described below
commit d4468d481f8aff3c8bdc5fd4c527004b4f27cf1d
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
