Databricks AI/BI Dashboard Parameters BREAKDOWN

If you want users to actually interact with Databricks AI BI Dashboards, you need to understand parameters. Filters are applied after a dataset runs. Parameters change the dataset SQL itself, meaning the filter gets applied before results are computed. That difference can save compute, speed up dashboards, and make your queries truly dynamic. In this breakdown, Zane Goodman shows how parameters work under the hood, how to create them directly inside a SQL dataset using the colon syntax, how to configure types and required default values, and how to wire parameters into dashboard controls. You’ll also see a real limitation with date range pickers and parameters, plus practical workarounds that still let you balance performance, flexibility, and cost. Finally, Zane explains the governance side: letting end users control parameters can re-run datasets and increase compute costs, so you need to decide when parameters should be user-facing. 👉 On-Demand Learning - Start With The FREE Community Plan: https://prag.works/odl-trial-yt 🔗Pragmatic Works On-Demand Learning Packages: https://pragmaticworks.com/pricing/ 🔗Pragmatic Works Boot Camps: https://pragmaticworks.com/boot-camps/ 🔗Pragmatic Works Hackathons: https://pragmaticworks.com/private-training/hackathons/ 🔗Pragmatic Works Virtual Mentoring: https://pragmaticworks.com/virtual-mentoring/ 🔗Pragmatic Works Enterprise Private Training: https://pragmaticworks.com/private-training/customized-enterprise-training/ 🔗Pragmatic Works Blog: http://blog.pragmaticworks.com/ 00:00 Why parameters matter for interactive dashboards 00:25 What we’re building: parameters wired to filters and SQL datasets 01:17 Filters vs parameters, when the filter is applied 03:01 Why parameters save compute on large datasets 04:11 Create a parameter in SQL using colon syntax 06:00 Configure parameter settings, type, single vs multi, default value 07:43 Date parameters, start date and end date between clause 11:22 Limitation: date range pick