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The following commit(s) were added to refs/heads/publish by this push:
new d326f475f8 Docs updated via
refs/heads/main:428802ee998818893b02e472c274305c78f97190
d326f475f8 is described below
commit d326f475f81944bc49471da1f29dd8276673566b
Author: GitHub Actions <[email protected]>
AuthorDate: Sat May 10 14:11:00 2025 +0000
Docs updated via refs/heads/main:428802ee998818893b02e472c274305c78f97190
---
_gen/packages-metadata.json | 10 +-
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 +-
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blog/airflow-survey-2024/index.html | 4 +-
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.../index.html | 4 +-
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.../index.html | 4 +-
blog/introducing_setup_teardown/index.html | 4 +-
.../index.html | 4 +-
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50 files changed, 174 insertions(+), 172 deletions(-)
diff --git a/_gen/packages-metadata.json b/_gen/packages-metadata.json
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index 091bd6e463..54649a3854 100644
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+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+01:00" />
<meta itemprop="wordCount" content="258"><meta itemprop="image"
content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />
diff --git a/use-cases/adobe/index.html b/use-cases/adobe/index.html
index 4b1e44d645..5e536f3c35 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="2025-05-09T09:10:39+01:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+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 d1440a4400..e20a76a97f 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="2025-05-09T09:10:39+01:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+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 3b2fe1f653..244f97b17b 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="2025-05-09T09:10:39+01:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+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 6790fc713d..5deb7fe177 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="2025-05-09T09:10:39+01:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+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 9e21883228..36c15af45f 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="2025-05-09T09:10:39+01:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+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 6448794d0d..b1fac96089 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="2025-05-09T09:10:39+01:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+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 1688c4a79b..369aab5358 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="2025-05-09T09:10:39+01:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+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 985b6aca52..ade070acb3 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="2025-05-09T09:10:39+01:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+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 4771bf103f..99e6cb010a 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="2025-05-09T09:10:39+01:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+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 b9cf5bac45..273eef4e1a 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="2025-05-09T09:10:39+01:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+01:00" />
<meta itemprop="wordCount" content="294"><meta itemprop="image"
content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />
diff --git a/use-cases/plarium-krasnodar/index.html
b/use-cases/plarium-krasnodar/index.html
index 6a64efa98a..3f7e53c5f0 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="2025-05-09T09:10:39+01:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+01:00" />
<meta itemprop="wordCount" content="257"><meta itemprop="image"
content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />
diff --git a/use-cases/seniorlink/index.html b/use-cases/seniorlink/index.html
index 4a151e5ff2..ae378c2ac7 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="2025-05-09T09:10:39+01:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+01:00" />
<meta itemprop="wordCount" content="400"><meta itemprop="image"
content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />
diff --git a/use-cases/sift/index.html b/use-cases/sift/index.html
index 3bfc14fa06..7072e42828 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="2025-05-09T09:10:39+01:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+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 6fac24fc31..b5b87a0e65 100644
--- a/use-cases/snapp/index.html
+++ b/use-cases/snapp/index.html
@@ -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="2025-05-09T09:10:39+01:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+01:00" />
<meta itemprop="wordCount" content="641"><meta itemprop="image"
content="/images/feature-image.png"/>
<meta itemprop="keywords" content="" />
diff --git a/use-cases/suse/index.html b/use-cases/suse/index.html
index 54b45a0333..9299d9c0a5 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="2025-05-09T09:10:39+01:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2025-05-10T15:08:51+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="2025-05-09T09:10:39+01:00" />
+<meta itemprop="dateModified" content="2025-05-10T15:08:51+01:00" />
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