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Posted to commits@airflow.apache.org by gi...@apache.org on 2025/09/25 21:06:41 UTC
(airflow-site) branch publish updated: Docs updated via refs/heads/main:286b0d574fef96c489fbaf0d6062aee9aa4d81ed
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new d872e0b613 Docs updated via refs/heads/main:286b0d574fef96c489fbaf0d6062aee9aa4d81ed
d872e0b613 is described below
commit d872e0b6133c23acd4fdbfdfe09e7dc6d82b4343
Author: GitHub Actions <ac...@users.noreply.github.com>
AuthorDate: Thu Sep 25 21:06:37 2025 +0000
Docs updated via refs/heads/main:286b0d574fef96c489fbaf0d6062aee9aa4d81ed
---
blog/airflow-1.10.10/index.html | 4 +-
blog/airflow-1.10.12/index.html | 4 +-
blog/airflow-1.10.8-1.10.9/index.html | 4 +-
blog/airflow-2.10.0/index.html | 4 +-
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blog/airflow-survey-2024/index.html | 4 +-
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blog/airflow_summit_2021/index.html | 4 +-
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.../index.html | 4 +-
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blog/introducing_setup_teardown/index.html | 4 +-
.../index.html | 4 +-
integrations.json | 2 +-
search/index.html | 4 +-
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51 files changed, 172 insertions(+), 172 deletions(-)
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index 1eb499857e..f8967cb531 100644
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index e9474cf7e8..153f62cb94 100644
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{
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index 5901eb0db3..16fed16b77 100644
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index 7c971183b2..eb8f7acf8e 100644
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<loc>/blog/airflow-three-point-oh-is-here/</loc>
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</url><url>
<loc>/blog/airflow-survey-2024/</loc>
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<loc>/blog/airflow-2.10.0/</loc>
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</url><url>
<loc>/blog/tags/vulnerabilities/</loc>
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</url><url>
<loc>/blog/fab-oid-vulnerability/</loc>
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</url><url>
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</url><url>
<loc>/blog/airflow-2.6.0/</loc>
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</url><url>
<loc>/blog/airflow-2.5.0/</loc>
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</url><url>
<loc>/blog/airflow-2.4.0/</loc>
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<loc>/blog/airflow-survey-2022/</loc>
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</url><url>
<loc>/blog/tags/airflow-summit/</loc>
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</url><url>
<loc>/blog/airflow_summit_2022/</loc>
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<loc>/blog/airflow-2.3.0/</loc>
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<loc>/blog/airflow_summit_2021/</loc>
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<loc>/blog/airflow-survey-2020/</loc>
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</url><url>
<loc>/blog/airflow-two-point-oh-is-here/</loc>
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</url><url>
<loc>/blog/experience-with-airflow-as-an-outreachy-intern/</loc>
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</url><url>
<loc>/blog/apache-airflow-for-newcomers/</loc>
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<loc>/announcements/</loc>
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<loc>/blog/implementing-stable-api-for-apache-airflow/</loc>
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</url><url>
<loc>/blog/tags/rest-api/</loc>
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<loc>/blog/airflow-1.10.8-1.10.9/</loc>
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</url><url>
<loc>/blog/tags/documentation/</loc>
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<loc>/blog/experience-in-google-season-of-docs-2019-with-apache-airflow/</loc>
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<loc>/blog/airflow-survey/</loc>
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</url><url>
<loc>/blog/announcing-new-website/</loc>
- <lastmod>2025-09-25T20:59:41+01:00</lastmod>
+ <lastmod>2025-09-25T22:04:04+01:00</lastmod>
</url><url>
<loc>/blog/apache-con-europe-2019-thoughts-and-insights-by-airflow-committers/</loc>
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<loc>/blog/tags/development/</loc>
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</url><url>
<loc>/blog/documenting-using-local-development-environments/</loc>
- <lastmod>2025-09-25T20:59:41+01:00</lastmod>
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<loc>/blog/its-a-breeze-to-develop-apache-airflow/</loc>
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</url><url>
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</url><url>
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diff --git a/use-cases/adjoe/index.html b/use-cases/adjoe/index.html
index de73e74461..e88b43e2ad 100644
--- a/use-cases/adjoe/index.html
+++ b/use-cases/adjoe/index.html
@@ -37,12 +37,12 @@ How did Apache Airflow help to solve this problem?" />
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<meta property="og:url" content="/use-cases/adjoe/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="adjoe">
<meta itemprop="description" content="What was the problem? Before adopting Airflow at adjoe, we handled job scheduling in two main ways: by setting up Kubernetes cronjobs or building AWS Lambda functions. While both approaches had their benefits, they also came with limitations, especially when it came to managing more complex workloads. As our data science teams needs evolved, it became clear that we needed a more robust and flexible orchestration tool.
How did Apache Airflow help to solve this problem?">
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
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diff --git a/use-cases/adobe/index.html b/use-cases/adobe/index.html
index a2a21ddcce..cd426ddb4c 100644
--- a/use-cases/adobe/index.html
+++ b/use-cases/adobe/index.html
@@ -36,11 +36,11 @@
<meta property="og:type" content="article" />
<meta property="og:url" content="/use-cases/adobe/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Adobe">
<meta itemprop="description" content="What was the problem? Modern big data platforms need sophisticated data pipelines connecting to many backend services enabling complex workflows. These workflows need to be deployed, monitored, and run either on regular schedules or triggered by external events. Adobe Experience Platform component services architected and built an orchestration service to enable their users to author, schedule, and monitor complex hierarchical (including sequential a [...]
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
<meta itemprop="wordCount" content="251"><meta itemprop="image" content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />
diff --git a/use-cases/adyen/index.html b/use-cases/adyen/index.html
index 8c733e2018..872a1bbefa 100644
--- a/use-cases/adyen/index.html
+++ b/use-cases/adyen/index.html
@@ -36,11 +36,11 @@
<meta property="og:type" content="article" />
<meta property="og:url" content="/use-cases/adyen/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Adyen">
<meta itemprop="description" content="What was the problem? Many years ago we started out with our own orchestration framework. Due to all the required custom functionality it made sense at the time. However, quickly we realized creating an orchestration tool is not to be underestimated. With the quickly increasing number of users and teams, time spent on fixing issues increased, severely limiting development speed. Furthermore, due to it not being open source, we constantly had to make [...]
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
<meta itemprop="wordCount" content="405"><meta itemprop="image" content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />
diff --git a/use-cases/big-fish-games/index.html b/use-cases/big-fish-games/index.html
index 3e17f36e6a..47ef7957fd 100644
--- a/use-cases/big-fish-games/index.html
+++ b/use-cases/big-fish-games/index.html
@@ -36,11 +36,11 @@
<meta property="og:type" content="article" />
<meta property="og:url" content="/use-cases/big-fish-games/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Big Fish Games">
<meta itemprop="description" content="What was the problem? The main challenge is the lack of standardized ETL workflow orchestration tools. PowerShell and Python-based ETL frameworks built in-house are currently used for scheduling and running analytical workloads. However, there is no web UI through which we can monitor these workflows and it requires additional effort to maintain this framework. These scheduled jobs based on external dependencies are not well suited to modern Big Data [...]
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
<meta itemprop="wordCount" content="336"><meta itemprop="image" content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />
diff --git a/use-cases/business_operations/index.html b/use-cases/business_operations/index.html
index 25ea1f32a7..e6a3a666a2 100644
--- a/use-cases/business_operations/index.html
+++ b/use-cases/business_operations/index.html
@@ -37,12 +37,12 @@ This video shows an example of using Airflow to run the pipelines that power a c
<meta property="og:type" content="article" />
<meta property="og:url" content="/use-cases/business_operations/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Business Operations">
<meta itemprop="description" content="Use Airflow for Business operations pipelines Airflow can be the starting point for your business idea! For many companies, Airflow delivers the data that powers their core business applications. Whether you need to aggregate user data to power personalized recommendations, display analytics in a user-facing dashboard, or prepare the input data for an LLM, Airflow is the perfect orchestrator.
This video shows an example of using Airflow to run the pipelines that power a customer-facing analytics dashboard.">
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
<meta itemprop="wordCount" content="425"><meta itemprop="image" content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />
diff --git a/use-cases/dish/index.html b/use-cases/dish/index.html
index 8ec32d15b6..ba756f7e28 100644
--- a/use-cases/dish/index.html
+++ b/use-cases/dish/index.html
@@ -36,11 +36,11 @@
<meta property="og:type" content="article" />
<meta property="og:url" content="/use-cases/dish/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Dish">
<meta itemprop="description" content="What was the problem? We faced increasing complexity managing lengthy crontabs with scheduling being an issue, this required carefully planning timing due to resource constraints, usage patterns, and especially custom code needed for retry logic. In the last case, having to verify success of previous jobs and/or steps prior to running the next. Furthermore, time to results is important, but we were increasingly relying on buffers for processing, wher [...]
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
<meta itemprop="wordCount" content="248"><meta itemprop="image" content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />
diff --git a/use-cases/etl_analytics/index.html b/use-cases/etl_analytics/index.html
index 3b505f5a65..afa0f2e60e 100644
--- a/use-cases/etl_analytics/index.html
+++ b/use-cases/etl_analytics/index.html
@@ -37,12 +37,12 @@ The video below shows a simple ETL/ELT pipeline in Airflow that extracts climate
<meta property="og:type" content="article" />
<meta property="og:url" content="/use-cases/etl_analytics/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="ETL/ELT">
<meta itemprop="description" content="Use Airflow for ETL/ELT pipelines 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.">
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
<meta itemprop="wordCount" content="300"><meta itemprop="image" content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />
diff --git a/use-cases/experity/index.html b/use-cases/experity/index.html
index 402f5eb127..5e02b3e478 100644
--- a/use-cases/experity/index.html
+++ b/use-cases/experity/index.html
@@ -37,12 +37,12 @@ How did Apache Airflow help to solve this problem? Ultimately we decided flexibl
<meta property="og:type" content="article" />
<meta property="og:url" content="/use-cases/experity/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Experity">
<meta itemprop="description" content="What was the problem? We had to deploy our complex, flagship app to multiple nodes in multiple ways. This required tasks to communicate across Windows nodes and coordinate timing perfectly. We did not want to buy an expensive enterprise scheduling tool and needed ultimate flexibility.
How did Apache Airflow help to solve this problem? Ultimately we decided flexible, multi-node, DAG capable tooling was key and airflow was one of the few tools that fit that bill.">
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
<meta itemprop="wordCount" content="191"><meta itemprop="image" content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />
diff --git a/use-cases/infrastructure-management/index.html b/use-cases/infrastructure-management/index.html
index 1f0fdfba30..c998b1c692 100644
--- a/use-cases/infrastructure-management/index.html
+++ b/use-cases/infrastructure-management/index.html
@@ -37,12 +37,12 @@ Infrastructure management is often needed within the context of other use cases,
<meta property="og:type" content="article" />
<meta property="og:url" content="/use-cases/infrastructure-management/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Infrastructure Management">
<meta itemprop="description" content="Use Airflow for Infrastructure Management Airflow can interact with any API, which makes it a great tool to manage your infrastructure, such as Kubernetes or Spark clusters running in any cloud. As of Airflow 2.7, the setup/teardown feature is available, a special type of task with intelligent behavior to spin up and tear down infrastructure at the exact time you need it.
Infrastructure management is often needed within the context of other use cases, such as MLOps, or implementing data quality checks.">
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
<meta itemprop="wordCount" content="310"><meta itemprop="image" content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />
diff --git a/use-cases/mlops/index.html b/use-cases/mlops/index.html
index a739b23472..eeb32ba3a5 100644
--- a/use-cases/mlops/index.html
+++ b/use-cases/mlops/index.html
@@ -36,11 +36,11 @@
<meta property="og:type" content="article" />
<meta property="og:url" content="/use-cases/mlops/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="MLOps">
<meta itemprop="description" content="Use Airflow for Machine Learning Operations (MLOps) Machine Learning Operations (MLOps) is a broad term encompassing everything needed to run machine learning models in production. MLOps is a rapidly evolving field with many different best practices and behavioral patterns, with Apache Airflow providing tool agnostic orchestration capabilities for all steps. An emerging subset of MLOps is Large Language Model Operations (LLMOps), which focuses on de [...]
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
<meta itemprop="wordCount" content="376"><meta itemprop="image" content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />
diff --git a/use-cases/onefootball/index.html b/use-cases/onefootball/index.html
index d58db98478..ccc50cab80 100644
--- a/use-cases/onefootball/index.html
+++ b/use-cases/onefootball/index.html
@@ -37,12 +37,12 @@ On top of that, new data tools appear each month: third party data sources, clou
<meta property="og:type" content="article" />
<meta property="og:url" content="/use-cases/onefootball/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Onefootball">
<meta itemprop="description" content="What was the problem? With millions of daily active users, managing the complexity of data engineering at Onefootball is a constant challenge. Lengthy crontabs, multiplication of custom API clients, erosion of confidence in the analytics served, increasing heroism (“only one person can solve this issue”). Those are the challenges that most teams face unless they consciously invest in their tools and processes.
On top of that, new data tools appear each month: third party data sources, cloud providers solutions, different storage technologies… Managing all those integrations is costly and brittle, especially for small data engineering teams that are trying to do more with less.">
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
<meta itemprop="wordCount" content="294"><meta itemprop="image" content="/images/feature-image.png"/>
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diff --git a/use-cases/plarium-krasnodar/index.html b/use-cases/plarium-krasnodar/index.html
index 8116a3f856..feb20a1ee5 100644
--- a/use-cases/plarium-krasnodar/index.html
+++ b/use-cases/plarium-krasnodar/index.html
@@ -36,11 +36,11 @@
<meta property="og:type" content="article" />
<meta property="og:url" content="/use-cases/plarium-krasnodar/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Plarium Krasnodar">
<meta itemprop="description" content="What was the problem? Our Research & Development department carries out various experiments, and in all of them, we need to create workflow orchestrations for solving tasks in game dev. Previously, we didn’t have any suitable tools with a sufficient number of built-in functions, and we had to orchestrate processes manually and entirely from scratch every time. This led to difficulties with dependencies and monitoring when building complex w [...]
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
<meta itemprop="wordCount" content="257"><meta itemprop="image" content="/images/feature-image.png"/>
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diff --git a/use-cases/seniorlink/index.html b/use-cases/seniorlink/index.html
index 2a313b6b95..248bb67691 100644
--- a/use-cases/seniorlink/index.html
+++ b/use-cases/seniorlink/index.html
@@ -37,12 +37,12 @@ We had built a robust stack of batch processes to deliver value to the business,
<meta property="og:type" content="article" />
<meta property="og:url" content="/use-cases/seniorlink/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Seniorlink">
<meta itemprop="description" content="What was the problem? Here at Seniorlink, we provide services, support, and technology that engages family caregivers. One of our focuses is using data to bolster our knowledge and improve the experience of our users. Like many looking to build an effective data stack, we adopted a Python, Spark, Redshift, and Tableau core toolset.
We had built a robust stack of batch processes to deliver value to the business, deploying these data services in AWS using a mixture of EMR, ECS, Lambda, and EC2.">
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
<meta itemprop="wordCount" content="400"><meta itemprop="image" content="/images/feature-image.png"/>
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diff --git a/use-cases/sift/index.html b/use-cases/sift/index.html
index 5cadd68f92..3c8fa8787c 100644
--- a/use-cases/sift/index.html
+++ b/use-cases/sift/index.html
@@ -36,11 +36,11 @@
<meta property="og:type" content="article" />
<meta property="og:url" content="/use-cases/sift/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Sift">
<meta itemprop="description" content="What was the problem? At Sift, we’re constantly training machine learning models that feed into the core of Sift’s Digital Trust & Safety platform. The platform gives our customers a way to discern suspicious online behavior from trustworthy behavior, allowing our customers to protect their online transactions, maintain the integrity of their content platforms, and keep their users’ accounts secure. To make this possible, we’ve built model traini [...]
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
<meta itemprop="wordCount" content="641"><meta itemprop="image" content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />
diff --git a/use-cases/snapp/index.html b/use-cases/snapp/index.html
index 2db697bc11..6ff946f01e 100644
--- a/use-cases/snapp/index.html
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@@ -36,11 +36,11 @@
<meta property="og:type" content="article" />
<meta property="og:url" content="/use-cases/snapp/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Snapp">
<meta itemprop="description" content="What was the problem? As the Map team at Snapp, one of the largest and fastest-growing internet companies in the Middle East, we have experienced significant growth over the past couple of years, expanding from a team of 7 to a team of 60. However, with this growth came the realization that some of our crucial tasks were being performed manually. This manual approach consumed valuable time and hindered our ability to execute these tasks efficiently.">
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
<meta itemprop="wordCount" content="641"><meta itemprop="image" content="/images/feature-image.png"/>
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diff --git a/use-cases/suse/index.html b/use-cases/suse/index.html
index a3c6e19184..e0786eaa2c 100644
--- a/use-cases/suse/index.html
+++ b/use-cases/suse/index.html
@@ -37,12 +37,12 @@ The construction of these containers requires the installation of several librar
<meta property="og:type" content="article" />
<meta property="og:url" content="/use-cases/suse/" /><meta property="og:image" content="/images/feature-image.png"/><meta property="article:section" content="use-cases" />
-<meta property="article:modified_time" content="2025-09-25T20:59:41+01:00" /><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-09-25T22:04:04+01:00" /><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="RancherBySUSE">
<meta itemprop="description" content="What was the problem? Our aim was to build, package, test and distribute curated and trusted containers at scale in an automated way. Those containers can be of any nature, meaning that we need a solution that allows us to build any kind of software with any kind of building tools like Maven, Rust, Java, Ant, or Go.
The construction of these containers requires the installation of several libraries (which may even conflict) and the orchestration of complex workflows with several integrations, executed either on a scheduled basis or triggered by events from external systems.">
-<meta itemprop="dateModified" content="2025-09-25T20:59:41+01:00" />
+<meta itemprop="dateModified" content="2025-09-25T22:04:04+01:00" />
<meta itemprop="wordCount" content="361"><meta itemprop="image" content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />