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new b86fb3e Adyen use-case (#488)
b86fb3e is described below
commit b86fb3e761c1a332c0a024dd763e5feb017fab50
Author: Jorrick Sleijster <[email protected]>
AuthorDate: Mon Oct 4 00:42:00 2021 +0200
Adyen use-case (#488)
---
landing-pages/site/content/en/use-cases/adyen.md | 19 ++++++++++++++
.../site/static/usecase-logos/adyen-logo.svg | 30 ++++++++++++++++++++++
2 files changed, 49 insertions(+)
diff --git a/landing-pages/site/content/en/use-cases/adyen.md
b/landing-pages/site/content/en/use-cases/adyen.md
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+++ b/landing-pages/site/content/en/use-cases/adyen.md
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+---
+title: "Adyen"
+linkTitle: "Adyen"
+quote:
+ text: "Apache Airflow is highly extensible and its plugin interface can be
used to meet a variety of use cases. It supports variety of deployment models
and has a very active community to scale innovation."
+ author: "Jorrick Sleijster"
+logo: "adyen-logo.svg"
+---
+
+##### 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 the effort ourselves to stay up to
date with the industry s [...]
+
+##### How did Apache Airflow help to solve this problem?
+Apache Airflow enabled us to extend upon the already existing operators and
sensors to make writing ETL DAGs as easy as possible. Within a couple minutes
of training, data scientists are able to write their own DAGs containing an
Apache Spark job and its corresponding dependencies. The Web UI allows our data
scientists to closely monitor the status and logs of the jobs so that they can
quickly interfere if something is not going as planned. We created our own
access groups such that tea [...]
+
+One powerful functionality of Apache Airflow is the ability to backfill. This
is helpful when new tasks are introduced or old jobs need to be rerun. By
creating our own plugin for Apache Airflow, we built a simple tool to
streamline back-filling. Besides clearing the runs, it also clears the
underlying data that was generated by the Spark Job. Coming from Apache Airflow
1.10, this plugin only required minor changes to support Apache Airflow 2.0.
+
+##### What are the results?
+We started out with having a full team just working on our orchestration tool.
With the help of Apache Airflow we managed to give the responsibility of
maintaining DAGs back to the data scientist teams. This allowed us to grow
quicker than ever to 20 teams that own in total approximately 200 DAGs and over
5000 tasks. In the meantime, our team has been able to extend Apache Airflow
further while also focussing on getting other new exciting technologies
on-board. With airflow, we now spend [...]
diff --git a/landing-pages/site/static/usecase-logos/adyen-logo.svg
b/landing-pages/site/static/usecase-logos/adyen-logo.svg
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