Joined June 2020
11 Photos and videos
Pinned Tweet
🎉 We're excited to announce the launch of Spectacles, the first continuous integration and automated testing tool for @LookerData. See what we’ve been building: spectacles.dev/post/introduc…

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🚀 Launching our Spectacles Looker Jobs Board! A dedicated platform connecting professionals with Looker-centric roles & companies. Find the perfect match in the Looker community: spectacles.dev/looker-jobs

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We're proud to feature fantastic companies like Ramp, Mentimeter, and Capital on Tap. Companies using Looker, submit your open roles to our board and connect with the talented candidates you need: spectacles.dev/looker-jobs-s…

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If you want to learn how to roll out LookML linting smoothly for a large team, look no further. The team at @epidemicsound launched linting as a mandatory check to a team of 20 developers in a couple weeks. spectacles.dev/post/lookml-l…

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Learn more about our brand new linter: x.com/SpectaclesCI/status/16…

Today, we’re launching a new validator for Spectacles—the Style Validator! It’s a linter for LookML that finds issues in PRs like measures without descriptions, views without primary keys, or views using the same SQL table, keeping your project tidy and maintainable.
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Spectacles retweeted
We're two weeks out from the next Analytics Engineering meetup in London at the @thoughtmachine office. 🙏Big thank you to our friends over @SpectaclesCI and @select_dev for letting us support such a great event! The countdown to grab your seat is on! 👉 bit.ly/3Izk2hj
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Today, we’re launching a new validator for Spectacles—the Style Validator! It’s a linter for LookML that finds issues in PRs like measures without descriptions, views without primary keys, or views using the same SQL table, keeping your project tidy and maintainable.
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Want to inspect only new code without having to clean up your whole project? Turn on incremental mode and Spectacles will only validate new or changed LookML.
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To get started with the Spectacles Style Validator, read our launch announcement! spectacles.dev/post/introduc…

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There aren't enough educational resources for technical Looker developers, platform admins, and large Looker teams. That's why, starting next week, we're launching a new webinar series, focused exclusively on @lookerdata. See the webinars: spectacles.dev/webinars

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The idea for the sessions is to regularly get the Looker community together in the same place and talk about actual hands-on experience with Looker. If you have something that you would be interested in presenting, we would love to hear from you! 🙏
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Our first session will be on November 30th. We’ll be talking about How To Fix Your LookML Project Structure, including how to use refinements to keep your code DRY and manageable. Register today! We hope to see you there. spectacles.dev/webinars/how-…

Spectacles retweeted
For the #data and #analytics folks, especially those using @googlecloud's Looker, just wanted to share that @SpectaclesCI is a great source of useful blog posts. "How to Fix Your LookML Project Structure" is a bit of a game changer: spectacles.dev/post/fix-your… #sql #techtwitter

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Why not good BI and good CI?

ALT Why Not Both Take Both GIF

Good BI is more valuable than AI. Good CI is more valuable than BI.
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Spectacles retweeted
15 Sep 2022
Finally landed on a LookML project structure I've liked with my current client and thankful I got the opportunity to write about it with data legends @_dylan_baker and @joshptemple 🙏🙏
We often hear complaints from our customers that their Looker projects are disorganized, hard to read, and are suffering from rampant, uncontrolled growth. Is there a better way? Have a look in our new blog post from @kenny_ning! spectacles.dev/post/fix-your…
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We often hear complaints from our customers that their Looker projects are disorganized, hard to read, and are suffering from rampant, uncontrolled growth. Is there a better way? Have a look in our new blog post from @kenny_ning! spectacles.dev/post/fix-your…

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Kenny's proposed project structure makes use of some underappreciated Looker features like refinements and filter-only views to create a robust Looker project structure that handles large teams and database schema changes with ease.
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Give the article a read and let us know: How does this compare to your team’s Looker project? How would you improve on this approach? spectacles.dev/post/fix-your…

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