Hands-on with an MCP for data qualityHow an MCP combined with the right metadata and context can supercharge data quality workflowsAug 26A response icon1Aug 26A response icon1
Hundreds of actionable data quality guidelinesBuilding a collection of bite-sized guidelines for data teams — from testing to ownership and incident management best practicesAug 18A response icon1Aug 18A response icon1
Using Omni’s AI Assistant on the Semantic LayerCan modern data tools’ AI assistant replace analysts for self-serve or is there still some way to go?Jul 18Jul 18
Using AI to build a robust testing frameworkAdding tests and monitors to dbt using Cursor, Claude, and dbt’s MCP serverJul 11Jul 11
Using AI for Data Modeling in dbtFrom raw data to a semantic layer with Cursor and dbt’s Fusion engineJul 4A response icon5Jul 4A response icon5
Benchmark Your Data TeamWhat data from hundreds of top data teams tells us about team size, role distribution, data-to-engineer ratios, and salaries.Feb 13A response icon1Feb 13A response icon1
A Guide for Building High-Quality Data Products68 pages of best practices on defining data products, testing strategies, managing ownership, and measuring data qualityJan 16Jan 16
Learnings from running hundreds of data incidents at SYNQHow treating issues and incidents differently has driven accountability and a trace of systemic issuesAug 26, 2024Aug 26, 2024
How top data teams are structuredA deep dive into the ratio of data roles across insight, data engineering, and machine learningJul 26, 2024A response icon15Jul 26, 2024A response icon15
