Unifying and supercharging your dbt transformations

The Challenge

dbt has revolutionized how analytics engineers transform data, bringing software engineering best practices to SQL. But often, dbt projects exist in a silo. You might use one tool for ingesting raw data, another for orchestrating your dbt runs, and yet another for complex transformations that can't be done in SQL (like advanced Python scripting or machine learning feature engineering). This fragmentation leads to a "stitch-it-together" approach, creating complex dependencies, maintenance headaches, and a lack of end-to-end visibility. It’s like having a brilliant chef (dbt) but needing separate kitchens, prep stations, and delivery services for every meal—inefficient and prone to miscommunication.

The Solution: Your Integrated dbt Powerhouse

Mage transforms dbt from a powerful, but often isolated, transformation tool into a core, seamlessly integrated engine within your broader data architecture. We provide a unified platform for data integration, transformation (with native dbt support), AI-powered development, and streaming pipelines—all within a collaborative, Git-native workspace. This means you can accelerate your dbt modeling and empower your analytics team with AI that helps models write models, reuses logic, and adapts to your needs in real time.

• Native dbt Integration, Enhanced Orchestration: Mage offers native support for running and managing your dbt models. You can execute dbt models individually, as groups, or all at once, and Mage automatically handles their dependencies, ensuring they run in the correct order. This moves beyond dbt Cloud's basic lineage view by providing a full drag-and-drop pipeline editor to visualize and manage your entire workflow.

• Multi-Language Synergy: The real magic happens when you mix and match dbt models with other blocks. You're no longer confined to SQL. Within the same Mage pipeline, you can combine dbt model runs with Python blocks for advanced data cleaning or feature engineering, R blocks for statistical analysis, and SQL blocks for direct database interactions. This eliminates siloed workflows and enables truly end-to-end pipelines.

• AI-Assisted dbt Development: Building and maintaining dbt projects becomes even smarter with Mage's AI Sidekick. It assists in generating code snippets, can help convert existing dbt configurations, and provides context-aware suggestions, speeding up your development process and making it easier to work with complex SQL transformations. This means less time writing boilerplate code and more time focusing on impactful analytics.

• Comprehensive Data Ingestion and Delivery: Unlike dbt Cloud, which focuses solely on transformation, Mage provides over 200 built-in connectors for seamless data ingestion from various sources. This means you can manage your entire ELT process—from extraction to loading, including your dbt transformations—all within one platform. Mage also handles the mapping between upstream pipeline outputs and your dbt models, automatically updating your mage_sources.yml file to reflect the latest available data.

• Full Observability, Dynamic Scaling, and Reliability: Mage offers full dynamic scheduling, monitoring, and alerting for your dbt models, meaning you don't need external orchestrators. You get native UI for logs, metrics, and traces, allowing you to quickly debug and optimize individual steps. Our platform provides auto-scaled execution on Kubernetes/ECS, handling thousands of concurrent jobs smoothly and efficiently, a significant advantage over dbt Cloud's limited concurrency.

• Centralized Control and Collaboration: Mage allows you to manage multiple dbt projects under a single control plane. With Git-backed version control, isolated workspaces, and enterprise-grade RBAC, teams can collaborate effectively, ensuring safer deployments across development, staging, and production environments.

Real-World Scenario: A Marketing Analytics Team's Unified View

Consider a marketing analytics team that uses dbt to transform raw customer interaction data into clean, aggregated models for reporting. They currently use dbt Cloud, but struggle to integrate the initial data ingestion from various ad platforms and CRM APIs, and also need to run Python-based attribution models before their dbt transformations.

With Mage, they can:

1. Ingest Raw Data: Build data integration pipelines using Mage's connectors to pull raw data from Facebook Ads, Google Analytics, and their CRM directly into their data warehouse.

2. Pre-dbt Python Transformations: Create Python blocks within the same Mage pipeline to run custom attribution logic or complex data enrichment that isn't practical in SQL.

3. Orchestrate dbt Models: Execute their existing dbt project directly within Mage. Mage automatically recognizes their dbt models and their dependencies, ensuring the Python output feeds seamlessly into the dbt transformations.

4. Post-dbt SQL/R Analytics: Add further SQL or R blocks for specific ad-hoc analysis or to create final dashboards, leveraging the clean, transformed data from dbt.

5. Monitor & Scale: Monitor the entire end-to-end pipeline, including both Python and dbt steps, from Mage’s unified UI. As marketing campaigns scale, Mage's auto-scaling ensures dbt models run efficiently without manual resource management.

By unifying their dbt transformations with Mage's broader data engineering capabilities, the marketing team gains complete control and visibility over their entire data lifecycle. They can move faster with trusted data, reduce operational complexity, and focus on delivering deeper insights, all from a single, intelligent platform.