SQL Blocks let you connect to data warehouses like BigQuery with minimal setup while maintaining the flexibility to write custom SQL. You can run either raw SQL (for multiple statements) or simple SQL queries, create/merge tables, and monitor execution output. Key best practices include organizing code, testing queries before deployment, and using version control. Common issues involve connection errors, SQL syntax problems, and data export failures - all of which can be resolved by checking configurations and logs.

This article guides you through the deployments and version control applications in Mage Pro. Both applications simplify how data engineers perform CICD. It explains how to connect Mage Pro to GitHub, use version control features to manage code changes, and configure deployment stages. The platform streamlines the entire workflow from repository setup to deployment, eliminating manual processes and ensuring consistent versioning across environments.

The guide provides a step-by-step tutorial for integrating Mage Pro SQL blocks with Databricks to create automated data pipelines. It walks through configuring Databricks credentials in Mage Pro, creating a pipeline, adding a data loader block to fetch API data (using golf rankings as an example), securely storing API keys, creating SQL blocks to transfer data to Databricks, and finally verifying the data through queries in Databricks. The integration aims to streamline data workflows by eliminating manual processes and creating a seamless connection between data sources and Databricks for analysis.

Securely move data from SFTP to Google BigQuery using Mage Pro for automated analytics. This guide walks you through setting up a pipeline, connecting to SFTP, choosing data streams, configuring replication, transforming data, connecting to BigQuery, triggering syncs, and verifying data. Automate your data flow, eliminate manual errors, and get valuable business insights!

Data teams in companies are drowning in too many different tools. Each tool does one thing well, but using them all together creates a mess that wastes time and money. Mage Pro fixes this by putting everything you need in one place instead of juggling separate programs like Airbyte, dbt Cloud, and others. With one system instead of many, teams spend less money on subscriptions, don't have to connect different tools together, work better as a team, and can adapt more easily when things change.

Accuracy tells you how many times the ML model was correct overall. Precision is how good the model is at predicting a specific category. Recall tells you how many times the model was able to detect a specific category.