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awesome-dbt
A curated list of awesome dbt resources
https://github.com/nszoni/awesome-dbt
Last synced: about 6 hours ago
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Get Started
- The Ultimate Guide to dbt - A comprehensive canvas guide to dbt, from the basics to advanced topics.
- dbt in a real world scenario, A Beginner dbt tutorial - A beginner tutorial to understand dbt with a real world example.
- Mastering dbt: Beginner to Pro - Paid Udemy course that covers theory, building a dbt project from scratch, and deploying to dbt Cloud.
- Analytics Engineering Glossary - Living collection of terms & concepts commonly used in the data industry by dbt Labs.
- Zero to Hero dbt - Complete course covering both theory & practice through real-world Airbnb use-case.
- Analytics Engineering with dbt - Paid course offered by co:rise covering the basics of dbt.
- dbt Fundamentals - Official free course offered by dbt. Excellent for learning the basics of dbt Cloud.
- Refactoring SQL for Modularity - Another dbt labs offered free course on dbt refactoring and CTE supercharging.
- Learn DBT from Scratch - Guides you through a setup paired with Snowflake (decorated with extras).
- Data Engineering Zoomcamp - Data engineering course on cutting edge tools including dbt.
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How To
- Discovery API use-cases - Use-cases and examples for the dbt Cloud Discovery API.
- dbt Docs as a Static Website - How to deploy dbt docs as a static website with App Engine and GitHub Actions.
- dbt Monorepo Workflow - How to get started with the team dbt workflow.
- Configuring Snowflake warehouse sizes in dbt - How to use dbt with Snowflake to allow specific warehouses to be chosen down to the model level.
- BigQuery Ingestion-Time Partitioning and Partition Copy With dbt - Combining ingestion-time partitioning and partition copy is a great way to achieve better performance for your models.
- Power up your data quality with grouped checks - How to use grouped checkes in dbt-utils to keep our data "on track".
- Dry running our data warehouse using BigQuery and dbt - Use dbt & BigQuery dry run jobs to validate our 1000+ models in under 30 seconds.
- Automatically generate ERD - Automatically generate ERDs and display in your docs site.
- Test SQL Pipelines against Production Clones using DBT and Snowflake - Leverage Snowflake Zero-copy-clones to run slim ci checks.
- How we structure our dbt projects - How the dbt team structures its dbt projects.
- How we structure our dbt projects - How the dbt team structures its dbt projects.
- dbt guide - Primer on how you should properly set up and configure your dbt workflow.
- Start Modeling Data - Configuring Bigquery with your dbt project.
- Accelerating Data Teams with dbt & Snowflake - A dbt & Snowflake workshop on financial data.
- Creating a dev environment quickly on Snowflake - Setting up teh integraton with Snowflake.
- How to set up a dbt data-ops workflow, using dbt cloud and Snowflake - Leverage GitHub Actions to set up CI/CD with dbt Core.
- How to configure your dbt repository - Mono-repo or not mono-repo?
- Best Practices for Optimizing Your dbt and Snowflake Deployment - Pocket guide on optimization best practices with Snowflake.
- How to Deploy dbt to Production using GitHub Actions
- Doing More With Less: Using DBT to load data from AWS S3 to Snowflake via External Tables - An alternative guide to set up your dbt-external-tables workflow.
- Best Practices for your dbt Style Guide - Standards for well organized base layer with Airbyte ingestion.
- Tips and Tricks about working with dbt - Tips from community members.
- Best Practices for your dbt Style Guide - Standards for well organized base layer with Airbyte ingestion.
- Writing Unit Tests for dbt - An overview about the package dbt-unit-testing.
- How to create Kimball dimensional models with dbt - A step by step guide on how to build Kimball dimensional models with dbt.
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- Best Practices for Leveraging Amazon Redshift and dbt - An overivew of best practices on how to integrate dbt with Redshift, that includes information about performance tunning and dbt code optimizations.
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- Business Intelligence Standards - Best practices in Business Intelligence standards for integrating with dbt.
- Jinja cheatsheet - Jinja cheatsheet for dbt development.
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- Best Practices for Optimizing Your dbt and Snowflake Deployment - Pocket guide on optimization best practices with Snowflake.
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- dbt Jinja Functions Cheat Sheet - Datacoves - Jinja Functions Cheat Sheet that covers the Jinja additions in dbt Core.
- dbt for Data Transformation – Hands-on - Yet another tutorial for using dbt Cloud.
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
- How to Deploy dbt to Production using GitHub Actions
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User Stories
- Analyzing Fishtown's dbt project performance with artifacts - Using project artifacts to identify anomalies and room for refactoring.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- Self-service Business Intelligence - Eliminate the need for a data modeling semantic layer in BI.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- Building an extension framework for dbt - How Monzo built an extension framework for dbt.
- Why I moved my dbt workloads to GitHub and saved over $65,000 - Save by replacing dbt Cloud with GitHub Actions.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- Self-service Business Intelligence - Eliminate the need for a data modeling semantic layer in BI.
- Leveraging DBT as a Data Modeling tool - Reflection on one-year usage of dbt.
- dbt + Materialize: Streaming to a dbt project near you - How to own your real-time transformation workflows like batch-based alternatives.
- Who's really using dbt? - Behind the community of analytics engineers.
- Deploying and Running dbt on Azure Container Instances - Demonstration of integration with Azure.
- Beware of DBT Incremental Updates Against Snowflake External Tables - Things you should be aware of when using external tables with dbt.
- dbt development at Vimeo - Best practises from the Vimeo Data team.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- Speeding up the dbt™ docs by 20x with React Server Components - A rewrite of dbt docs using Next.js with React Server Components and SSG.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- dbt and the Analytics Engineer — what's the hype about - Behind the upheaval of the analytics engineer profession.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- "Semantic-free" is the future of Business Intelligence - How to leverage dbt as a data catalog and semantic layer (joins, synonyms, etc.) that BI tools can just plug into.
- “Is This You?” Entity Matching in the Modern Data Stack with Large Language models - An experiment in productionizing LLMs.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
- How HomeToGo connected dbt and Superset to make metadata more accessible and reduce analytical overhead - A dbt<>Superset connector that leverages Superset's API capabilities and dbt's manifest.
-
Integrations
- Grai - Expose warehouse dbt tests in CI to upstream data consumers so production changes never break the warehouse.
- Datafold - Gives a quick print out summary of changes so you can move fast and (not) break stuff!
- Cube - APIs, Caching, and Access Control on top of dbt Metrics.
- FlexIt Analytics - Business Intelligence platform with deep dbt Cloud and CLI integration.
- Raycast dbt Jobs - Raycast integration to monitor dbt Cloud Jobs.
- Metaplane - Data Observaibility layer on top of your dbt + BI project.
- Dbt + Machine Learning: What makes a great baton pass? - Landscape of ML utilities around dbt.
- Soda - Integration of Soda's data observability platform and dbt.
- Supported Adapters - Offically supported database adapters.
- Superset - Open source visualization layer for your Modern Data Stack.
- Dagster and dbt: Better Together - Getting started with the dagster-dbt library.
- Privacy Dynamics - Anonymize data in your dbt project.
- dbt-streamdeck - Stream Deck plugin enables you to view the status of models and jobs as actions in your Stream Deck.
- modal-dbt - This repo gives some code to run dbt jobs/actions using modal which is a serverless application framework.
- Lightdash - Open source Looker alternative with deep dbt integration.
- fal - Add multi-language support (Python) to your dbt project.
- prefect-dbt - Collection of Prefect integrations for working with dbt with your Prefect flows.
- Auto Alert - diqu - Automate and streamline the alerting/ notification process for dbt test results using this versatile CLI companion tool. Receive detailed alerts & test metadata seamlessly on various platforms, promoting improved collaboration on dbt project issues 🐞🚀.
- Tabula - Tabula is an end-to-end automation platform for data management tasks.
- Acryl DataHub - A unified data catalog, governance, and observability platform. Use it to view models, docs, test results, and column-level lineage across your dbt projects and downstream dashboards.
- Raycast dbt Metadata - Queries the dbt Cloud API to return some useful information about your models (number of tests, time they took to run etc…).
-
Data Quality
- BigQuery Stale data detection - Stale data detection with dbt and BigQuery dataset metadata.
- Environment-dependent Unit Testing in dbt - Guide on how to run unit tests in dbt dynamically.
- re_data - A dbt package for montioring metrics and detect anomalies.
- How do you test your data - Suggestions on testing your data powered by the community.
- How to unit test sql transforms in dbt - Unit test using source defer and generic custom tests.
- dq-tools - Make simple storing test results and visualisation of these in a BI dashboard leveraging 6 Data Quality KPIs.
- PipeRider - PipeRider allows you to define the shape of your data once, and then use the data checking functionality to alert you to changes in your data quality.
- Elementary - A dbt package that provides data anomaly detection as dbt tests.
- dbt-expectations - Port between dbt and great_expectations to extend out-of-the-box tests.
- DataKitchen Open Source Data Observability - Data breaks. Servers break. dbt and other tools break. Observability and alerting across and down your data estate. Save time with simple, fast data quality test generation and execution.
-
CI/CD
- How to Create CI/CD Pipelines for dbt Core - This post dives into the use of CI/CD for dbt Core, providing insights on dbt Cloud's Slim CI CICD job pattern and how to implement this using dbt Core.
- Slim CI/CD with Bitbucket Pipelines - How to setup slim CI on Bitbucket.
- dbt-docs-to-notion - A GitHub action for exporting dbt model docs to a Notion database.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- How to review an analytics pull request - Checkpoints to consider when reviewing an analytics engineer PR.
- Performing a blue/green deploy of your dbt project on Snowflake - A very tidy and fail-safe way to run dbt in production by using two parallel production enviromnents.
- How we speed up our CI runs by 10x using Slim CI - Limit data in long-running CI checks to improve developing experience.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- dbt-ci-cd - This setup is designed to demonstrate and implement best practices for testing and deploying dbt models.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- How to review an analytics pull request - Checkpoints to consider when reviewing an analytics engineer PR.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
- Continuous Integration and Automated Build Testing with dbtCloud - Great and detailed blogpost on setting up Slim CI in dbt Cloud.
-
Orchestration
- Building a Scalable Analytics Architecture with Airflow and dbt - Leveraging the dbt manifest in Airflow.
- Auto-generating an Airflow DAG using the dbt manifest - Yet another article on extracting value from the manifest file.
- Building a robust data pipeline with the dAG stack: dbt, Airflow, Great Expectations - Demonstration of a data orchestration project with Airflow.
- Run dbt in Azure Data Factory - Primer about dbt on Azure Data Stack.
- Orchestrate dbt Core jobs with Airflow and Cosmos - Run your dbt Core projects as Apache Airflow DAGs and Task Groups.
-
Utilities
- Turntable VSCode extension - A handy docs composer and column-level lineage.
- Jinjat - Low-code application framework that turns your dbt projects into web apps.
- dbt-model-finder - A Streamlit web app to find currently running dbt models.
- dbtc Explorer - A Streamlit web app to explore the dbt Cloud API.
- VSC - Wizard for dbt Core - This extension accelerates your first-time environment setup with dbt Core, and optimizes your continual development of transformation pipelines.
- Run changed models based on Git status - Handy bash function to run changed models since last commit.
- How we set up our computers for working on dbt projects - Things I wish I would have known when started working with dbt. Tools and hacks to improve developing experience.
- Your Essential dbt Project Checklist - Checklist on items necessary for a successful dbt project.
- dbt Style Guide - Developing styleguide often referred in PR templates.
- Clean your warehouse of old and deprecated models - Clean out warehouse models which are not existent in the project.
- tdb - A sweet and speedy code generator for dbt.
- dbt-command-center - A local web application that provides a user-friendly interface to monitor and manage dbt runs.
- dbt-score - Linter for dbt metadata.
- dbt-llm-tools - RAG based LLM chatbot for dbt projects.
- turboYAML - An AI-powered CLI tool for converting dbt SQL files to YAML using OpenAI.
- dbt-exposures-crawler - Automate the creation of dbt exposures from different sources.
- dbt-loom - A dbt-core plugin to weave together multi-project dbt-core deployments.
- dbt-meshify - A dbt-core plugin that automates the management and creation of dbt groups, contracts, access, and versions.
- dbot - An LLM-powered chatbot with the added context of the dbt knowledge base.
- dbt-lineagex - A Column Level Lineage Graph for dbt.
- fst: flow state tool - A tool to help you stay in flow state while developing dbt models.
- dbt_tld - A self-updating dbt library that will maintain a list of current IANA/ICANN recognized top level domains.
- dbt-feature-flags - Feature Flags in dbt models.
- dbtpal - A Neovim plugin for dbt model editing.
- cookiecutter-dbt - Cookiecutter template for dbt projects.
- turbovault4dbt - TurboVault4dbt is an open source tool that automatically generates dbt models according to datavault4dbt-templates.
- dbtvault-generator - Generate DBT Vault files from yml metadata (supporting `dbtvault` package).
- dbt-container-skeleton - All the basics to get a nice containerized dbt development environment.
- oliver-twist - DAG auditing tool that audits the DBT DAG and generates a summary report.
- dbt-sql-formatter - Makes your sql less bad.
- dbterd - CLI to generate DBML file from dbt manifest.json.
- dbt-cue - Generate dbt yml files using the CUE language.
- dbt-artifacts-parser - It enables us to deal with catalog.json, manifest.json, run-results.json and sources.json as python objects.
- GitHub Action: Cancel Running CI Job - This allows to always have the newest code commit running in the CI job without having to wait for the stale job runs to finish.
- dbtc - Unaffiliated python interface to various dbt Cloud API endpoints.
- dbt-osmosis - Enhance the developer experience significantly with workbench, output diffs, and YAML management.
- pytest-dbt-core - Pytest dbt core is a pytest plugin for testing your dbt projects.
- looker-gen - Generate lookml from dbt.
- dbtenv - A version manager for dbt.
- sqlfmt - This tool formats your dbt SQL code so you don't have to.
- SQLFluff - SQL linter that supports dbt and Jinja templating.
- Build Data Access Layer on dbt - Package to build GraphQL API on top of your dbt project.
- fzf-dbt - Search dbt models interactively from terminal.
- dbt-tips - Excellent companion to your dbt practice with rich collection of tips.
- Pre-commit hooks - Pre-commit hooks for checking data integity before schema change commit.
- dlt(data load tool) - The open-source Python library for data loading.
- datapilot - AI teammate for engineers to ensure best practices in their SQL.
- vscode-dbt-power-user - VSCode extension to give more clarity on model dependencies.
- Understanding the scopes of dbt tags - Understanding the scopes of dbt tags.
- diff2docs - Turn your diff into docs with the help of GPT-4o.
-
Packages
- dbt-excel - A dbt adapter for working with Excel.
- dbt-coves - CLI tool for generating a scaffold for your dbt project.
- dbt-profiler - Data profiling and doc block generator.
- dbt_utils - General macros library. A must have.
- dbt-artifacts - This package builds a mart of tables from dbt artifacts loaded into a table.
- dbtvault - Package for generating and executing ETL for Data Vault 2.0.
- dbt_audit_helper - Macros for data audits that compare columns values and schemas between tables.
- dbt-dag-monitoring - A dbt package for monitoring airflow DAGs and tasks.
- dbt-data-diff - Data-diff solution for dbt-ers with Snowflake.
- dbt-tags - Tag-based masking policies management in Snowflake.
- dbt-testgen - Generate dbt tests based on sample data.
- dbt_otel_export - Takes dbt runs and turns them into OpenTelemetry traces.
- dbt-assertions - Package to assert rows in-line with dbt macros.
- dbt-ibis - Write your dbt models using [Ibis](https://ibis-project.org/), the portable Python dataframe library.
- dbt-timescaledb - The TimescaleDB adapter plugin for dbt.
- dbt-fake - Daily updated fake data for dbt learning and projects.
- dbt_cloud_run_cost - Package to calculate dbt Cloud usage-based cost.
- dbt-reconfigured - A dbt package containing reconfigured macros.
- dbt-census-utils - A collection of dbt macros for working with Census data.
- dbt-fabric - A dbt adapter for working with Microsoft Fabric Data Warehouses.
- dq-vault - Data Quality Observation of Data Vault layer.
- dbt-translate - Translate numbers into words.
- dbt-excel - A dbt adapter for working with Excel.
- dbt_linreg - Linear regression in SQL using dbt.
- dbt-snowflake-query-tags - Automatically tag dbt-issued queries with informative metadata.
- snowflake-resource-monitoring - Yet another package to monitor Snowflake usage.
- usagedata - Provides insights on the database/table level usage informations from Snowflake.
- dbt_ml - Package for dbt that allows users to train, audit and use BigQuery ML models.
- ddbt - This repo represents my attempt to build a fast version of DBT which gets very slow on large projects (3000+ data models). This project attempts to be a direct drop in replacement for DBT at the command line.
- dbt-snowflake-monitoring - A dbt package to help you monitor Snowflake performance and costs.
- datavault4dbt - Macros for staging and creation of all DataVault-Entities you need, to build your own DataVault2.0 solution.
- DDO - Perform DataOps & administrative CI/CD on your data warehouse.
- dbt-yaml-check - Checks that columns defined in YAML also exist in SQL.
- data-diff - A command-line tool and Python library to efficiently diff rows across two different databases.
- dbt-project-evaluator - This package highlights areas of a dbt project that are misaligned with dbt Labs' best practices.
- dbt_constraints - Generate database constraints based on the tests in a dbt project.
- dbt-date - Date logic and calendar functionality.
- dbt-privacy - Macros to make it easier to protect your customers' data.
- dbt-fivetran-utils - General macros and helpers.
- dbt_metrics - Macros to support secondary calculations and generate business metrics.
- dbt-metabase - Model synchronization from dbt to Metabase.
- dbt-ml-preprocessing - A SQL port of python's scikit-learn preprocessing module, provided as cross-database dbt macros.
- dbt-external-tables - Macros to stage your external sources.
- dbt-feature-store - Macros to build a feature store right within your dbt project.
- dbt-codegen - Macros that generate dbt code, and log it to the command line.
- dbt-init - Create a project and populate as much of the dbt project as possible.
- dbt-artifacts - This package builds a mart of tables from dbt artifacts loaded into a table.
- dbt-erdiagram-generator - This packages generate ERD diagrams from a dbt project.
- dbt2looker - Generate Looker views for dbt models.
- dbt-coverage - Checks dbt docs and tests coverage.
- dbt-meta-testing - Yet another coverage testing.
- dbt-superset-lineage - Push and pull metadata between dbt to Superset.
- dbt-invoke - CLI for creating, updating, and deleting dbt property files.
- dbt-unit-testing - Package which contains macros to support unit testing.
- Terraform-dbt Cloud Module - IAC in dbt Cloud via Terraform.
- dbtvault - Package for generating and executing ETL for Data Vault 2.0.
-
Community
- Data Council Austin 2023 - A conference for data teams.
- State of Analytics Engineering 2023 - A survey of pains, gains, and areas of investment for global data teams.
- dbt Labs Tiktok - Official TikTok channel of dbt Labs.
- Locally Optimistic - A Slack community of aspiring analytics leaders discussing and sharing lessons learned and challenges from their experiences in using data.
- DataTalks.Club - Global online community of data enthusiasts. Podcasts and blogs, etc. are distributed with high frequency.
- Metadata Weekly - Weekly substack about metadata, the metrics layer and MDS.
- Data & Analytics Events in 2022 - Great curated list of upcoming data analytics conferences.
- Data Council Austin 2022 - Worldwide community driven analytics conference with a handful of talks fitting to the dbt stack.
- Discourse v2 - Revamped and ported hub of main discussions for the community.
- Coalesce 2021 - Second iteration of the analytics engineer conference.
- Coalesce 2020 - Annual dbt conference full of fascinating use-cases.
- dbt meetups - List of community led dbt meetups.
- Analytics Engineer Roundup - Official dbt Labs newsletter on topics of the MDS.
- Benn Stacil's Newsletter - Tought-provoking reads from founder of Mode.
- Data Engineering Weekly - Weekly newsletter of recent trends in Data Engineering.
- Data Engineering Podcast - One of the most popular data engineering podcasts covering great concepts and new products.
- Analyitics Engineer Podcast - Official podcast of dbt Labs.
- dbt Slack - Energy-filled hub of analytics engineers (Highly recommended).
- r/dataengineering - Subreddit of data engineering topics.
- Drill to Detail Podcast - Special guests discussing big data, business intelligence, modern data stack.
- dbt meetups - List of community led dbt meetups.
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Sample Projects
- Analytics Engineer Survey 2023 - Repo containing data and dbt template of the survey.
- Tracking the Fake GitHub Star Black Market with Dagster, dbt and BigQuery - Explore the topic of fake GitHub stars.
- aws athena x dbt - How to build a small and modern data infrastructure.
- image - Data Build Tool (dbt) for Effective Data Transformation on AWS
- datafold-dbt-ci-advanced - Add 4 more integrations to your dbt CI pipeline: Slim CI, pre-commit hooks, Data Diffs, and Slack notifications.
- NBA_Data_Modeling - Using the official NBA API, this repo explains how to ingest, store, transform, and serve insights.
- awesome-public-dbt-projects - A curated list of awesome public dbt projects.
- mdsinabox - MDS in a box deployed anywhere.
- gee_production_gatekeepers - A demonstration of production gatekeepers in Snowflake and BigQuery.
- dbt-beyond-the-basics - A repository demonstrating advanced use cases of dbt in multiple areas.
- mdsfest-opensource-mds - Demo project for open source MDS.
- f1-data-pipeline - F1 Data Pipeline.
- transfermarkt-datasets - E2E dbt project for scraping and transforming football data from Transfermarkt.
- dbt_github_archive_bigquery - A dbt project for GitHub Archive data on BigQuery.
- dbt_workspace - A workspace template for dbt demos.
- Cloud Cost Monitoring - A dbt project to monitor cloud costs.
- Data-aware orchestration - Dagster's ability to create a global dependency graph between different dbt projects.
- attribution-playbook - A worked example to demonstrate how to model customer attribution.
- mrr-playbook - A worked example to demonstrate how to model subscription revenue.
- Use dbt inside Visual Studio Code development containers - Set up your dbt environment with pre-installed extensions.
- dag-stack - Dbt-Airflow-GreatExpectations Stack.
- Spotify User Analytics - Sample dbt project with Spotify user data.
- dbt-github-workflow - Deploy BigQuery + Airflow.
- airflow-dbt-demo - Demonstration of Airflow integration.
- GitLab Data Team - GitLab's open source dbt project.
- Jaffle Shop - A self-contained dbt project for testing purposes.
- Part 1 – Redshift
- Part 3 – EMR on EC2
- Part 4 – EMR on EKS
- Part 5 – Athena
- Part 2 – Glue
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Snippets
- trange_join - Macro to join dbt snapshots.
- dbt_to_dbdiagram - Generate an ERD via dbdiagram.io from a dbt project.
Programming Languages
Categories
Sub Categories
Keywords
dbt
50
snowflake
11
python
11
dbt-packages
8
data
8
sql
7
bigquery
5
cli
5
package
4
redshift
4
dbt-core
4
testing
4
postgresql
3
dbt-cloud
3
dbt-artifacts
3
dataops
3
data-science
3
data-reliability
3
data-quality
3
dataquality
3
data-pipeline
3
prefect
3
business-intelligence
3
data-engineering
3
data-observability
3
data-visualization
2
lineage
2
postgres
2
data-lineage
2
github-actions
2
documentation
2
analytics
2
metabase
2
dbt-macros
2
data-profiling
2
looker
2
dbt-tests
2
tool
2
erd
1
dbml
1
d2
1
python3
1
kafka
1
dag-auditing
1
dag
1
workflow
1
fast
1
flowstate
1
hot-reload
1
graphviz
1