Learn how to seamlessly connect your dbt models with Mage Pro pipelines to automate data workflows and load data into BigQuery. This step-by-step tutorial guides you through creating batch pipelines, configuring YAML files, and running dbt transformations in Mage Pro.

Migrations are usually scary, but they don’t have to be. Moving your dbt Cloud project to Mage Pro is almost as simple as cloning your dbt project into Mage Pro and running a pipeline. You can be fully migrated in about 10 minutes and start connecting your dbt models to Mage Pro blocks. This is a smooth transition

Mage Pro delivers an all-in-one data platform for extraction, transformation, and loading with real-time processing and multi-language support, while dbt Fusion specializes in ultra-fast advanced data transformation. Choose Mage Pro for simplicity and complete pipelines, or dbt Fusion for maximum transformation performance with external tool integration

How a solo developer at TechTree built a full-scale data warehouse using Mage, AI, and DBT—no team required. See Caleb’s journey from chaos to control.

We'll build a complete real-time data pipeline that captures live cryptocurrency trading data from Binance, process it through Apache Kafka, and transforms it using Mage Pro for analytics and storage in BigQuery. This demonstrates how modern streaming architectures handle high-frequency financial data with sub-second latency and zero message loss.

Learn how to seamlessly integrate Anthropic’s Claude API with MCP and Mage Pro to automate workflows and unlock advanced prompt engineering. This step-by-step guide covers setup, code examples, and best practices for building smarter, AI-powered tools—perfect for developers and teams looking to boost productivity and stay ahead in the world of AI automation.

Solstice Energy’s small data team automated complex reporting with Mage Pro, cutting errors and stress while empowering junior staff-all without heavy infrastructure.

This article describes how to build an AI system to answer questions about the Great Attractor using Model Context Protocol (MCP). In part 1 you’ll learn how to use Mage Pro to create a data pipeline that connects knowledge sources to AI systems, including fetching PDF data, cleaning the text, and preparing it for us with Antrhopic’s Claude. This approach allows for more accurate, source-specific AI responses rather than relying solely on general training data.