Building a data stack is akin to lining up a winning sports team. The data decision-maker is the team’s coach. Trophies are data quality, reliability, business insights, ML models, dashboards, etc. Coaches define their strategy, selecting players based on their capabilities. They make sure they fit together in a delicate choreography. All of this within budget and staffing constraints. Data decision-makers perform the same exercise, with their data stack components.

This is a difficult exercise. Money can’t buy your way to the Champions’ League. Likewise, you can’t buy your way out of data problems. …


Have you ever wondered how to choose between Snowflake, BigQuery or Redshift? What’s best between Tableau and Looker? To whom a data team should report? Who to hire first: a Data Engineer or a Data Scientist?

A Data Leader reflecting on Tableau vs. Looker. Original: Le penseur de la Porte de l’Enfer (musée Rodin), by Jean-Pierre Dalbéra from Paris, France, wikimedia.

If you’re familiar with these questions, we know how you feel. If these decisions impacted your work, we experienced that too. And if you couldn’t help someone struggling with them, we bet what’s following could prevent that next time.

The Modern Data Network’s objective is to build a community to inform data teams’ decision-making. We’ll achieve it by sharing experience and learning from each…

Modern Data Network

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store