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Another round of tidiness after @houqp weighed in on the PR
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_posts/2020-02-19-modernizing-an-old-data-pipeline.md

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Our massive data pipeline has helped us process enormous amounts of information
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over the past decade, all to help us help our users discover, read, and learn.
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In this blog series, I will share how we're upgrading our data pipeline to
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give internal customers faster, and more reliable results.
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In this blog series, I will share how we're upgrading the pipeline to
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give internal customers faster and more reliable results.
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The data pipeline is currently managed using a home grown workflow
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orchestration system written in Ruby called "Datapipe." The first commit of our
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reduce coupling and speed up development cycles.
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**Ownership:** Today, we have dedicated engineers keeping eyes on nightly runs to
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notify workflow owners if anything goes wrong. The web UI doesn't some of the
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notify workflow owners if anything goes wrong. The web UI doesn't support some of the
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common maintenance actions like killing a running tasks. This, combined with
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lack of built-in monitoring and alerting support within the orchestration
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system, means even if workflow owners want to take full ownership of their

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