dbt Labs

You: shows up to the meeting in this sweater 🎄 The team: quietly opens 12 tabs of charts and metrics

Looking to connect, learn, and have fun with the dbt Community IRL? Check out these upcoming dbt Meetups happening around the globe: 🇹🇼 Taipei | Wednesday, January 8th, organized by Karen Hsieh, Laurence Chen, Allen Wang, and LI KUAN LIAO 🇺🇸 Raleigh | Wednesday, January 22nd, organized by dbt Labs (Bolaji Oyejide 🔥🎙) 🇺🇸 Austin | Wednesday, January 22nd, organized by dbt Labs (Tyler Rouze) 🇧🇪 Ghent | Thursday, February 20th, organized by Sam Debruyn and Lise Kerckhove dbt Meetups are gatherings dedicated to helping you own your analytics engineering workflow. RSVP now and tag a friend below to invite them along 👯♂️ https://lnkd.in/ekknesFN

It's 12 days of Christmas, but for data teams 🎄💜⛄️

💡 Assumptions can only take you so far. Real insights come when the data tells the full story. Samiksha Gour from SurveyMonkey shares how her team uses surveys to uncover the real challenges they face, like tackling data quality issues and streamlining data processing. As Samiksha put it: “Don’t go by assumptions, go by data.” Read the blog (link in comments) to learn how SurveyMonkey is teaming up with dbt Cloud and Monte Carlo to turn insights into action. 

The 2025 State of Analytics Engineering survey closes in two days. Have you taken it yet? 👀 This is your last chance to influence the report for analytics engineers. Your wins, struggles, and insights could shape how data teams think in 2025. 👉 Take the survey https://lnkd.in/dnkh-2zX And while you’re here, let's make things interesting: Which SQL operator currently rules your world? 

How to scale analytics, straight from the experts. 💡 Ten data leaders share their best advice on building adaptable, enterprise-wide data strategies. Key insights include: “Embedding data experts in each business domain allows them to align directly with the context, ensuring that data is both relevant and actionable.” -Raman Singh, Symend “DevOps—or its spinoff DataOps—is a must-have nowadays. It’s easy to build new solutions, but hard to maintain and scale them in the long run.” -João Bernardo Pires Antunes, Roche “As you scale, lean on your analytics engineers and their skillset to flex into infrastructure problems that might traditionally call for a DevOps engineer.” -Katie Claiborne, Duet For more tips like these, check out the guide: https://lnkd.in/eH4vwFdw

It’s time to strengthen your (dbt) Core 💪 There are some exciting updates to dbt snapshots in dbt Core v1.9. Here’s what’s new: 1️⃣ YAML configurations: Simplify your setup by replacing Jinja blocks with YAML-based configurations. 2️⃣ Customizable metadata column names: Rename snapshot metadata columns to fit your needs. 3️⃣ Flexible current row values: Define what “current” means for your data. It’s your call. Snapshots have been a cornerstone feature since dbt’s early days, and now they’re even more powerful and adaptable to your workflows.

This is a great step-by-step guide to setting up dbt Cloud for the enterprise. Thank you South Shore Analytics for the blueprint 🙌

🧪 Test smarter, not harder. Here’s how to think about testing at every layer of your pipeline: 🔍 Sources Start at the beginning. Tests here ensure the data entering your pipeline is complete and consistent, catching issues early. 🔄 Staging The staging layer is where raw data gets cleaned and standardized. Testing at this stage ensures assumptions like field formats and mappings hold true before moving downstream. 🔗 Intermediate models This is where business logic comes into play. Tests here verify joins, aggregations, and calculations so errors don’t cascade into final outputs. 📊 Marts At the final layer, tests validate that your insights reflect the metrics and definitions your stakeholders rely on. Testing smarter at every layer builds trust in your data and saves time downstream. Check out the blog by Jerrie Kumalah Kenney and Faith McKenna (Lierheimer) (link in comments) to learn more.

As AI adoption accelerates, many enterprises are considering whether they need a Chief Artificial Intelligence Officer. But is this new role the key to AI success, or is there a better way forward? In this BigDATAwire article, Mark Porter shares his perspective: AI implementation and success should not be the responsibility of one individual but rather a shared endeavor across the organization. Mark explores why the role of a "Chief AI Officer" might not be the solution—and provides practical guidance on how companies can empower their teams to integrate AI into everyday operations more effectively. Read the full article for more: https://lnkd.in/gPpTUiDP