dbt Labs

“All we needed to leverage for our customer-facing visualizations in our B2B portal was the data. Once we centralized data transformations with dbt and their Semantic Layer, we could easily create the visualizations to our front end. It became super simple." By migrating off BI tool embeds, Bilt Rewards achieved 80% cost savings while empowering their teams with faster, more reliable insights. See how they transformed their data workflows: https://lnkd.in/gWY_F3BH

🧪 Test smarter, not harder. We’re breaking down the 4 layers of data testing in your pipeline. First up: the sources layer. At this layer, tests should surface issues that can be fixed at the source system. If a test flags something that isn’t fixable at the source, remove it and address the problem in your staging layer instead. What to test at the sources layer: Freshness • 🚨High-priority sources: Use dbt source freshness, set severity to error, and fail jobs if freshness fails. • ⚠️Lower-priority sources: Set severity to warn—track freshness without breaking pipelines. Data hygiene Focus on identifying issues that are fixable in the source system, such as: • 🗂️ Duplicate customer records that can be removed at the source. • ❌ Nulls (e.g., missing names or emails) that should be filled in at the source. • 🔑 Duplicate primary keys that can be resolved upstream. Testing smarter at the sources layer builds trust in your data and prevents unnecessary complexity downstream.

DuckDB redefines how analytical compute happens—handling massive CSVs and Parquet files effortlessly, right on your local machine. It’s optimized for low-latency, exploratory workflows, bridging back-end data processing with front-end UIs. 🎙️ In the latest episode of the Analytics Engineering Podcast, Hamilton Ulmer from MotherDuck shares how DuckDB is becoming essential for modern data visualization and analysis. Catch the full episode to explore how DuckDB is reshaping analytics workflows (link in comments).

👉 Swipe through to see how you can make the most of dbt Explorer, and read the blog to dive deeper into these tips https://lnkd.in/gk3Sd64x Which of these tips are you using today? What are you going to try out now that you know about it?

Scaling analytics doesn’t have to feel overwhelming. 10 data leaders share practical advice for building scalable, reliable data strategies. Here are some key takeaways: “Focus on the key problems that drive the most value for the business, and then treat those problems like modular building blocks that you use to build all the analytics out of.” — Scott G. Parent, Eleanor Health “Once you’re transparent and once you get more people involved into your data project, then you will see data analysts and analytics engineers be born in other teams.” — Valentinas Mitalauskas, Hostinger "Make it really easy for everyone at your company to resolve at least 80% of the questions that come in.” — Kyle Salomon, LiveRamp Read all 10 tips here: https://lnkd.in/eH4vwFdw

AI is already reshaping data workflows. Where do you think it will have the biggest impact in 2025?

Migrating extensive data to the cloud is no small feat. Warner Bros. Discovery did it without breaking a sweat—or their dashboards. How? By taking a measured, dbt-powered approach that delivered value along the way. Here’s what made their strategy successful: ☁️ A phased migration strategy Instead of moving everything at once, Warner Bros. Discovery started with foundational datasets and expanded from there—adopting a "core-to-cloud" approach. 🛠️ Refactoring business logic with dbt The team transformed years of complex, legacy SQL into clean, modular, and reusable transformations—laying a scalable foundation for their cloud workflows. 📊 Minimizing disruption to the business By using testing, version control, and incremental transformations, they maintained data quality and consistency so stakeholders could keep trusting their dashboards. Read the full story here: https://lnkd.in/gK7fN-re

As 2024 comes to a close, what does the future hold for data teams in 2025? Tristan Handy, our co-founder and CEO, shares his take on four trends shaping the year ahead: 🧊 Open table formats are about to take off Adoption of Iceberg and other open formats has been slow, but the momentum is building. With dbt Labs, EL tools, and the data clouds making implementation easier, expect adoption to accelerate—fast. ⚡ The rise of utility compute Purpose-built engines for specific workloads are changing the game. Fivetran’s free ingest for Iceberg tables shows what’s possible: ultra-optimized engines that absorb costs for narrow, well-defined workloads. More vendors will follow suit in 2025. 🧩 Multiple compute environments, but a unified layer on top More enterprises are diversifying compute platforms. The real question: where does the unified view of the data estate live? Metadata catalogs? User-facing tools? This will be the key space to watch. 🔗 Consolidation and end-to-end workflows. Every data platform—Snowflake, Databricks, Amazon Web Services (AWS)—is going more “end-to-end” to own the full user experience. As open table formats give customers a choice on where workloads run, the competition will shift to who controls the end-to-end workflow. Read Tristan’s full predictions here: https://lnkd.in/edTdRMVf

If you’re an analyst who doesn’t quite get what dbt is yet, or want to understand how it can help you, this is for you. Two analysts share what changed when they made the switch to dbt: ⚙ From queries to reusable models No more one-off SQL. With dbt, queries become modular, reusable transformations that scale. 🛠️ Version control Git is baked into dbt, making it easy to collaborate, track changes, and roll back when needed. 🧪 More trust with testing In dbt, you can quickly add tests to your output for accuracy and reliability—no more finding issues from a dashboard. 🗂️ Easier documentation dbt lowers the lift around maintaining documentation and viewing lineage so your work is accessible to the entire team, improving data literacy. Read the blog by Rachael Gilbert and Chris Fiore to learn more: https://lnkd.in/eUu2R3D7

New year, new me… or maybe just new data goals. What’s your data New Year’s resolution for 2025? Share yours below ⬇️