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new f8a6b5b docs: fix spelling (#356)
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commit f8a6b5b65aa4381148d91956f99f38172f56d914
Author: John Bampton <jbamp...@users.noreply.github.com>
AuthorDate: Fri Dec 18 20:51:34 2020 +1000
docs: fix spelling (#356)
---
landing-pages/site/content/en/use-cases/experity.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/landing-pages/site/content/en/use-cases/experity.md
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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. Having it based on open
source and python were large factors that upheld our core principles. At the
time, Airflow was missing a windows hook and operator so we contributed the
WinRM hook and operator back to the community. Given its flexibilty we also use
DAG generators to have our metadata drive our DAGs and keep maintenance costs
down.
+Ultimately we decided flexible, multi-node, DAG capable tooling was key and
airflow was one of the few tools that fit that bill. Having it based on open
source and python were large factors that upheld our core principles. At the
time, Airflow was missing a windows hook and operator so we contributed the
WinRM hook and operator back to the community. Given its flexibility we also
use DAG generators to have our metadata drive our DAGs and keep maintenance
costs down.
##### What are the results?
We have a very flexible deployment framework that allows us to be as nimble as
possible. The reliability is something we have grown to trust as long as we use
the tool correctly. The scalability has also allowed us to decrease the time it
takes to operate on our fleet of servers.
