rachael-ds commented on a change in pull request #373:
URL: https://github.com/apache/airflow-site/pull/373#discussion_r562770626
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File path: landing-pages/site/content/en/use-cases/plarium-krasnodar.md
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@@ -0,0 +1,17 @@
+---
+title: "Plarium Krasnodar"
+linkTitle: "Plarium Krasnodar"
+quote:
+ text: "Apache Airflow helps us efficiently tackle crucial game dev tasks,
such as working with churn or sorting bank offers."
+ author: "David Manukian"
+logo: "plarium-krasnodar-logo.svg"
+---
+
+##### 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 workflows. We needed a tool that would
provide a more centralized approach so that we could see all the logs, the
number of retries, and the task performance time. The most important thing that
we lacked was the ability to backfill historical data and restart failed tasks.
+
+##### How did Apache Airflow help to solve this problem?
+Apache Airflow offers lots of convenient built-in solutions, including
integrative ones. The DAG model helps us avoid errors and follow general
patterns when building workflows. In addition, this platform has a large
community where we can find plenty of sensors and operators that cover 90% of
our cases. This allows us to save ourselves loads of time.
Review comment:
This is awesome - from my experience, not everyone gets what operators
are/why are they useful until they start using them. This is such a minor point
on the last sentence, feel free to ignore :)
_This saves us time by reducing the amount code we need to write and
maintain._
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