dbt Labs

Let’s unlock the power of AI with dbt Cloud—download our guide to learn how. 🔓⚡️ You’ll access insights like: • AI’s impact on data teams: Discover how AI is reshaping the responsibilities and workflows of data teams • Overcoming AI deployment challenges: Understand the common challenges in integrating AI and how dbt Cloud simplifies these complexities • Tools for ensuring data integrity: Explore dbt Cloud’s features that help maintain high-quality, secure data for AI applications • How leading companies are using AI to enhance productivity and decision-making through dbt Cloud Get your copy here: https://lnkd.in/gZSmJKn4

"As an analytics engineer, I might not have taken the job if dbt was not already in the picture. It's like asking a surgeon to come work at your hospital, which doesn't have an operating room built yet." - Samuel Holden Garfield, Staff Analytics Engineer at Retool Retool uses dbt Cloud to empower its employees to make their own decisions and build their own tools using data. Retool's Head of Growth (who is not a data professional) set up dbt Cloud to generate value out of large volumes of data generated by their product. Retool uses dbt Cloud for: - data quality checks and testing that caught issues they didn't know to look for - rich documentation that explained what the data meant, where it was, and how to use it - integration with Databricks to take advantage of Databricks' ML features

Data enthusiasts: You know what a semantic layer is and why it’s useful… But what use cases does it support? 🤔 Early adopters of the dbt Semantic Layer are taking advantage of consistent metrics across their businesses—and 5 key use cases are emerging💡 1. Reporting & BI 2. Embedded analytics 3. AI / LLM integrations 4. Self-serve analytics 5. Exploratory analytics Get an in-depth look at these use cases in our latest article: https://lnkd.in/gBFDG7XG

You know a Semantic Layer would be hugely valuable, but how do you actually build such a thing? Pro tip: Crafting a Semantic Layer is about building iterative velocity alongside accuracy, so that when your stakeholders ask about Revenue MoM grouped by Attribution Channel, you can answer instead of adding a ticket to the backlog. Start with these four steps: 1. Identify a Data Product that is impactful: Find something that is in heavy use and high value, but fairly narrow scope. Don’t start with a broad executive dashboard that shows metrics from across the company because you’re looking to optimize for migrating the smallest amount of modeling for the highest amount of impact that you can. For example, a good starting place would be a dashboard focused on Customer Acquisition Cost (CAC) that relies on a narrow set of metrics and underlying tables that are nonetheless critical for your company. 2. Catalog the models and their columns that service the Data Product, both in dbt and the BI tool, including rollups, metrics tables, and marts that support those. Pay special attention to aggregations as these will constitute metrics. 3. Melt the frozen rollups in your dbt project, as well as variations modeled in your BI tool, into Semantic Layer code. 4. Create a parallel version of your data product that points to Semantic Layer artifacts, audit, and then publish. Creating in parallel takes the pressure off, allowing you to fix any issues and publish gracefully. You’ll keep the existing Data Product as-is while swapping the clone to be supplied with data from the Semantic Layer. Dig deeper into the step-by-step process of how to ship a Semantic Layer in pieces at our link in the comments.

The future is here. Take the first step toward making AI insights accessible to everyone in your organization at our virtual event "Boost AI Reliability with dbt Cloud" ⚡️ Join Drew Banin, Azzam Aijazi, Jason Ganz, and Luis Leon as we pull back the curtain to demystify AI and LLMs for data teams. You can expect: • Actionable Insights for Success: Learn how to easily build reliable AI-powered data experiences. • Real-World Solutions: Discover how dbt Cloud users solve real-world problems with Generative AI. • Introducing dbt Assist: See our new AI-enabled workflow (now in beta) that quickly generates documentation and tests in dbt Cloud, boosting productivity and data quality. • In-depth Demos: Experience the end-to-end workflow in action, featuring "Ask dbt," a new feature of our Snowflake Native App powered by the dbt Semantic Layer and Snowflake Cortex. Save your spot now: https://bit.ly/4ePAU1n

Join us for a fireside chat 🪵🔥 with data experts from RMIT University and the University of Canterbury. We’ll discuss the challenges, triumphs, and future of data management, integrity, and innovation in academia. Learn from leading data professionals transforming the educational data environment: Sarah Taylor, Darren W., and Cameron Mair. 🗓️ Thursday, 8 August 2024 ⏰ 11.30AM - 12.30PM AEST / 1.30PM - 2.30PM NZST Save your spot now: https://lnkd.in/gxAXKejN

Happy birthday to us—we just turned 8 years old! 🎂 Our employees are the heartbeat of our company and the reason we get to celebrate this milestone together. Enjoy these snapshots of our celebration across the world with some delicious dbt(reats) 🎉

A sneak peak of the Coalesce 2024 agenda is here 👀 The world’s biggest celebration of data is right around the corner. Get ready for breakout sessions and peer exchanges like: • How leading businesses like Virgin Media O2, Warner Bros. Discovery, and Houston Food Bank are implementing dbt at scale to ship trusted data faster • AI unlocked: Delivering business value and ROI • Insights to invites: How data gurus win friends and influence stakeholders We’re also bringing back the hands-on trainings and certifications that sell out every year. Grab your spot while they last. Check out the initial set of breakout sessions (and keep an eye out as we announce more in the coming weeks): https://lnkd.in/g9Nt6XWx

dbt Cloud 🤝 Datavault 🤝 AutomateDV (formerly known as dbtvault) How do dbt Cloud and Data Vault work together? Enhanced Visibility and Governance: Combining dbt and AutomateDV is smart—they work seamlessly together. dbt features included in the dbt Mesh offering include data security, lineage and contracts as well as model versioning support many of the paradigms also built-in to Data Vault. All of this is defined using files, so it is all tracked in version control as well. Scalability and Efficiency: AutomateDV on dbt Cloud supports scalable Data Vault components and has been tried and tested in larger companies and production environments. Combined with enterprise features in dbt Cloud, it provides effective management of extensive Data Vault projects. Higher Standards and Reliability: The dbt ecosystem of packages and features provide a solid foundation for doing Data Vault projects consistently and to a high standard with less maintenance overhead. dbt Cloud streamlines development, ensuring speed without sacrificing quality. Paired with Data Vault's pattern-based approach, it lets developers focus on business needs, not just technical tasks. Learn how to streamline your data warehouse development with this quickstart guide by Alex Higgs, AutomateDV Product Manager at Datavault: https://lnkd.in/e7KYMuVP

Roche uses dbt Cloud to scale its data platform. Here’s Yannick Misteli, Head of Engineering, Global Product Strategy at Roche, on: • moving from dbt Core to dbt Cloud • building data products that are reusable • bringing data mesh principles to Roche with dbt Mesh