getpiperider

PipeRider is now Recce.
Follow us at @DataRecce or @datarecce.bsky.social

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Myth 4: Data Quality is Just About Monitoring

Data quality is not just about monitoring; it requires a proactive strategy. Version control, impact analysis, testing, and gradual deployment are key. Modern tools like PipeRider embed quality into workflows. 🚀

#DataQuality

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Myth 3: It’s Difficult to Track Changes and Lineage

Tools like PipeRider integrated with dbt make tracking data lineage and changes easy. Visual lineage graphs and lineage diffs provide visibility into data flows and impacts. 📈

#DataLineage #DataQuality

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@JohnKutay Seinfeld (easily my most rewatched show)
Columbo
Halt and Catch Fire

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@VFisa @streamlit @keboola @getdbt Did you record the stream by any chance?

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@tall_tree_desu We've love to know more about your experiences. How are you using the profiler? Data exploration? Profile compare?

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@PastorSotoB1 @DataTalksClub That's awesome, please share your project when you finish - we'll have some swag left over for those that use PipeRider in the final project

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@DeutschJush @DataChaz Chaz is not a bot, he's a MACHINE!

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@a_escontrela @DataTalksClub Well done Alicia 🎉

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@A3_Alia 2/2

Our GitHub workflow for using PipeRider in GitHub Actions should help you understand the step-by-step process:

https://t.co/QbGak3OIdZ

The related documentation on using PipeRider in GitHub Actions/CI can be found here:

https://t.co/uftmSRLG1d

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@A3_Alia 1/2
Hi Alia, thanks for pointing this out.

The current method we advise would be:

- Install dbt-cloud-cli
- Run your dbt-cloud job and obtain the run ID
- Use the run ID to download the run artifacts
- Run PipeRider against the downloaded artifacts