dbt Labs | LinkedIn (original) (raw)

Software Development

Philadelphia, PA 98,257 followers

The creators and maintainers of dbt

About us

dbt Labs is on a mission to empower data practitioners to create and disseminate organizational knowledge. Since pioneering the practice of analytics engineering through the creation of dbt—the data transformation framework made for anyone that knows SQL—we've been fortunate to watch more than 20,000 companies use dbt to build faster and more reliable analytics workflows. dbt Labs also supports more than 3,000 customers using dbt Cloud, the centralized development experience for analysts and engineers alike to safely deploy, monitor, and investigate that code—all in one web-based UI.

Industry

Software Development

Company size

201-500 employees

Headquarters

Philadelphia, PA

Type

Privately Held

Founded

2016

Specialties

analytics, data engineering, and data science

Products

dbt

ETL Tools

dbt is a transformation framework that enables analysts and engineers collaborate with their shared knowledge of SQL to deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. dbt’s analytics engineering workflow helps teams work faster and more efficiently to produce data the entire organization can trust.

Locations

Employees at dbt Labs

Updates

💡 What’s the real value of data? In the latest episode of The Analytics Engineering Podcast, Tristan Handy and 📚 Cedric Chin unpack the traditional view of data as a tool for answering questions—and why that perspective often falls short. Cedric highlights a common challenge: data professionals going above and beyond to deliver answers… only to see those insights go unused. How can we shift the narrative to make data truly impactful for businesses? 🎙️ Listen to the full conversation here: https://lnkd.in/gCvSwwP2What’s your take—how can we ensure data work leads to real outcomes? 👇
Procuring enterprise software is one thing. Getting your team up and running with it to deliver outsized results? That’s the real challenge. To help you get there, we’ve put together a guide on how to introduce dbt Cloud in your organization and ensure successful adoption at scale. It breaks down the process into eight practical steps: 1️⃣ Align with company objectives: Map analytics problems to key business goals. 2️⃣ Benchmark and propose outcomes: Identify gaps in current processes and set measurable improvement goals. 3️⃣ Identify solutions: Compare tools based on outcomes and alignment with your goals. 4️⃣ Define a proof of value (POV): Focus on time-bound use cases to measure impact. 5️⃣ Tabulate results: Record key results against your baseline for two weeks. 6️⃣ Present findings: Summarize outcomes like increased uptime, productivity, and data trust scores. 7️⃣ Create the implementation plan: Develop a phased approach to migration. 8️⃣ Prepare to ramp: Enable more users to participate with automated documentation and community support. This guide is your roadmap for turning plans into action—and making a measurable impact with dbt Cloud. Check out the full guide to learn more https://lnkd.in/gVmAuTuf
Data folks, fill in the blank: If I had a dollar for every time ______, I’d be rich by now 💸
Machine learning workflows often hit bottlenecks when it comes to feature engineering. But with Snowflake’s Feature Store and dbt Cloud, you can streamline the entire process—from feature creation to deployment. Here’s how it works: • Start by building feature tables as dbt models. For example, create rolling aggregations like transaction counts or sums for the past 1, 7, or 30 days—all with dbt macros that keep your code DRY (Don’t Repeat Yourself). • Use Snowflake’s Feature Store to register entities and feature views, making features easily discoverable and shareable across teams. • Generate training datasets with point-in-time accuracy, ensuring your ML models get the right features at the right time. • Finally, train and deploy models entirely within Snowflake, seamlessly retrieving features for inference. This integration doesn’t just simplify ML workflows; it bridges the gap between data engineering and machine learning, enabling faster, more reliable pipelines for predictive modeling. Dive into the step-by-step guide on our blog (link in comments) by Randy Pettus and Luis Leon.
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We’re heading back to Vegas—this time as an #AWSreInvent sponsor 🎰💰🌆 Here’s where you can find us: 📍 Stop by booth 1795: Watch live demos and see how dbt Cloud and Amazon Web Services (AWS) can help simplify your workflows. 🤝 Book a one-on-one meeting: Sit down with a dbt expert to get tailored advice and insights to enhance your data strategy. https://lnkd.in/g8hf5rqb📣 Join our lightning session: On Wednesday, December 3rd at 4:00 p.m. in Theater 1, join Connor McArthur and Sri Kamireddy to hear how Moderna uses dbt Mesh to bring people and data together.
Brightside Health transformed its data strategy by centralizing metrics with the dbt Semantic Layer 📊 Join dbt Labs product manager Jordan Stein with Brightside Health’s chief data officer Hans Nelsen and data engineer Fidel Ilustre to learn how centralizing data, embedding analytics, and following best practices can improve data reliability and scalability across teams. The virtual talk will take place Wednesday, November 20 from 10-10:45am PT. Save your seat ➡ https://lnkd.in/g_KKfTwX
Data practitioners—your voice is more essential than ever 🚀 As the data landscape shifts, we're at a pivotal moment of introspection. How do we ensure our work stays compelling, aligned, and impactful? How do we keep data products at the forefront of driving value for our organizations? That’s exactly what the 2025 State of Analytics Engineering Survey is here to uncover. This is your opportunity to share your team's pains, gains, and strategic investments—and help us capture the pulse of data teams worldwide. Make your voice heard—share your insights 🗣️ https://lnkd.in/gvTgPXUH
Set your team up for success with dbt Cloud with these actionable tips: • Define success metrics: Clarify who your stakeholders are, how frequently the data needs to be updated, and any SLAs for data freshness and quality. • Implement secure access: Use single sign-on (SSO) and role-based access control to ensure secure, streamlined access for your team. • Use global connections in dbt Cloud: Opt for OAuth or native warehouse authentication to simplify credential management. • Leverage the data delivery insights dashboard (beta): ➡ Test coverage: Check if models have adequate tests—encourage teams to add more if coverage is low, ensuring data reliability. ➡ Project activity: Track how many models were built in the last 90 days to gauge project engagement. ➡ Source freshness: Identify any stale data sources and flag them to keep your data up-to-date. You can dive deeper into these tips by reading the blog from dbt Labs product manager Neha Palacherla Hystad or watch Neha’s #Coalesce2024 session on-demand (link in comments).
Come for the memes, stay for the networking and helpful dbt hacks. Join the dbt Community 👉 https://lnkd.in/dnMVYqdp to share the joys and the pain of data transformation with others who get it.
We all know that data jobs are constantly evolving. The crucial question is: In what ways will they change? 🤔 Erik Bernhardsson, CEO and co-founder of Modal, stopped by the Analytics Engineering Podcast (https://lnkd.in/girGtCsW) and shared his thoughts. Here are some of Erik’s takes on what we need to see from data jobs of the future: • Staff should move away from “shuffling data around.” Instead, everyone should be using data to drive value for the business. • Internal platform teams are “ephemeral and transient.” In the future, Erik hopes that organizations just use open source vendors, which will reduce the need for dedicated teams managing internal systems. • Are you a data engineer, data scientist, or analytics engineer? To Erik, “it doesn’t matter.” Most important is the work itself. Erik predicts the need for individuals to engage with data, AI, and machine learning will continue to grow, but that specific job titles will change over time. What do you think? Let us know in the comments ⬇️

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dbt Labs | LinkedIn (original) (raw)

Software Development

Philadelphia, PA 98,257 followers

The creators and maintainers of dbt

About us

Products

dbt

ETL Tools

Locations

Employees at dbt Labs

Updates

Join now to see what you are missing

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