Posts

Showing posts from July, 2025

Connecting DBT to Snowflake

  Connecting DBT to Snowflake: A Step-by-Step Guide with Best Practices Introduction In the modern data stack, DBT and Snowflake are a powerful combination. DBT enables modular, version-controlled data transformations using SQL, while Snowflake provides a scalable, cloud-native data warehouse. Connecting the two allows data teams to build reliable, testable, and automated pipelines that deliver clean, analytics-ready datasets. Whether you're using DBT Cloud or DBT Core (CLI) , this guide will walk you through the connection process and highlight best practices for configuration, security, and collaboration. Step 1: Set Up Your Snowflake Account Before connecting DBT, you need access to a Snowflake account. If you're new to Snowflake: Sign up for a trial account via Snowflake’s website. Choose a cloud provider and region that aligns with your organization’s infrastructure. Create a virtual warehouse for DBT transformations (e.g., transforming ). Create databases and schemas ...

A Deep Dive into dbt debug and Logs

Mastering Logging and Debugging in DBT: A Deep Dive into dbt debug and Logs Introduction In the fast-paced world of data engineering, where pipelines are expected to run reliably and deliver accurate insights, the ability to debug and troubleshoot effectively is not just a technical skill—it’s a survival tool. Whether you're building a new model, integrating a source, or deploying a production job, things can and will go wrong. And when they do, DBT (Data Build Tool) provides a powerful set of tools to help you figure out what happened, why it happened, and how to fix it. At the heart of DBT’s troubleshooting toolkit are two essential components: the dbt debug command and the DBT log files . Together, they offer a window into the inner workings of your DBT project, helping you diagnose configuration issues, runtime errors, and performance bottlenecks. In this blog, we’ll explore how logging and debugging work in DBT, what kind of information you can extract, and how to use thes...

Understanding Its File and Folder Structure - DBT

Inside a DBT Project: Understanding Its File and Folder Structure Introduction In the world of modern data engineering, DBT (Data Build Tool) has emerged as a transformative solution for managing data transformations in a scalable, modular, and version-controlled way. Unlike traditional ETL tools that often rely on opaque workflows and proprietary interfaces, DBT embraces the principles of software engineering—treating data transformations as code. One of the key reasons DBT is so effective is its well-defined project structure . Every DBT project is organized into a set of folders and configuration files that serve specific purposes. This structure not only promotes clarity and collaboration but also enables automation, testing, documentation, and deployment. In this blog, we’ll take a deep dive into the anatomy of a DBT project, exploring the purpose and importance of each folder—such as models, seeds, snapshots, and macros—and how they work together to create a robust data t...