Mage Blog

Mage SQL Blocks empower data engineers to streamline workflows, boost productivity, and tackle complex data transformation challenges, making it suitable for both SQL novices and experienced users.

The article explores the transformative capabilities of Mage Pro SQL Blocks, an efficient tool designed for data engineers to streamline SQL-based data operations.

Prepping data for dimensional models is essential for successful Business Intelligence (BI). From transforming raw data and implementing techniques like Slowly Changing Dimensions (SCD) and snapshots, to ensuring data quality through validation and testing, these steps lay the groundwork for reliable and insightful analytics. Get these steps right, and your dimensional model will be set up to deliver precise, business-focused insights.

The tutorial guides readers through setting up Mage, generating sample medical data, and building each layer using SQL transformations, demonstrating how to progress from raw data ingestion to creating analytics-ready datasets.

This article explores how Mage addresses the challenges of financial intuitions through features like modular architecture, configurable rules engines, and scalable processing. It also outlines best practices for maintaining regulatory flexibility, including regular reviews, cross-functional collaboration, and continuous monitoring.

Conditional blocks in Mage are powerful tools for creating dynamic, decision-making data pipelines. This article explains how to implement conditional blocks, using a banking example of processing Suspicious Activity Reports (SARs) for transactions over $10,000.

This article explains data integration’s importance in modern business and introduces five leading platforms: Informatica PowerCenter, Fivetran, Prefect, Talend, and Airbyte. It outlines some key features from each too, highlighting various approaches to data integration — from enterprise solutions to open-source alternatives. These platforms offer diverse capabilities like visual workflow design, automated schema management, and extensive pre-built connectors. By exploring these tools, readers gain insights into current data integration technologies and how they address complex data management challenges in today’s data-driven business landscape.

Organizations face challenges managing vast amounts of fragmented data. Centralized data systems using integration pipelines and incremental models offer a practical solution. These systems unify data, improve quality, and enhance efficiency. Incremental models process only new or updated data, reducing computation time and costs. This approach enables faster decision-making, better resource optimization, and improved analytics capabilities. While implementation can be complex, the long-term benefits make it a valuable strategy for organizations dealing with large-scale, frequently updated data.

Global hooks in Mage are a powerful feature that allow executing custom code before or after API operations. They provide flexibility to extend functionality, integrate with external systems, validate data, and more across different components of your application. With targeting conditions and asynchronous execution, global hooks offer granular control and performance optimization.

In this tutorial, we integrate dbt with Mage to create a data pipeline, moving data from a source to a PostgreSQL database and performing SQL transformations through staged models. By setting up Docker and PostgreSQL, and following a step-by-step process, we effectively manage data orchestration and analytics using Mage and dbt.