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.

A grounded comparison of two data pipeline platforms, based on real-world migration conversations

Master Google Cloud data engineering with this in-depth guide. Learn GCP tools, best practices, certification paths, and how to advance your career.