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new e1fb0d5 Add Seniorlink to Use Cases (#462)
e1fb0d5 is described below
commit e1fb0d56f0798b3107bff036882908a479621d97
Author: Christopher Petrino <[email protected]>
AuthorDate: Thu Aug 5 05:22:51 2021 -0400
Add Seniorlink to Use Cases (#462)
---
.../site/content/en/use-cases/seniorlink.md | 21 +++++++++++++++++++++
.../site/static/usecase-logos/seniorlink-logo.png | Bin 0 -> 6835 bytes
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b/landing-pages/site/content/en/use-cases/seniorlink.md
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+---
+title: "Seniorlink"
+linkTitle: "Seniorlink"
+quote:
+ text: "Airflow helped us increase the visibility of our batch processes,
decouple our batch jobs, and improve our development cycle, all while building
confidence in our ability to scale and grow."
+ author: "Christopher Petrino"
+logo: "seniorlink-logo.svg"
+---
+
+##### 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. Moving fast, as many new endeavors do, we ultimately ended up
with one monolithic batch process with many smaller satellite jobs. Given the
scale and quantity of jobs, we began to lose transparency as to what was
happening. Additionally, many jobs were launched in a single EMR cluster and so
tightly coupled that a failure in o [...]
+
+We were beginning to lose precious time manually managing the schedules via
AWS Datapiplines, AWS Lambdas, and ECS Tasks. Much of our development effort
was spent waiting for the monolith to complete running to examine a smaller job
within. Our best chance at keeping system transparency was active documentation
in our internal wiki.
+
+##### How did Apache Airflow help to solve this problem?
+Airflow gave us a way to orchestrate our disparate tools into a single place.
Instead of dealing with multiple schedules, we have a straightforward UI to
consider. We gained a great deal of transparency, being able to monitor the
status of tasks, re-run or restart tasks from any given point in a workflow,
and manage the dependencies between jobs using DAGs. We were able to decouple
our monolith and schedule the resulting smaller tasks confidently.
+
+##### What are the results?
+Airflow increased the visibility of our batch processes through the use of the
DAGs and the UI. Our end-to-end run time decreased by 20%, given our ability to
decouple our monolithic batch jobs into several smaller ones. Our development
and debugging time reduced with our ability to manage and isolate all our
tasks. We were able to merge our diverse toolset into a more central location.
Lastly, with its broad adoption, we were able to quickly push this new
framework up to our production [...]
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