Power BI Architecture Choices for Azure Databricks (w/ Liping Huang)

LIVESTREAM DATE/TIME 📅

October 16th 2026 - 9:30 AM (Pacific Time)

DESCRIPTION 📄

Power BI gives you four main ways to report on data that lives in Azure Databricks: Direct Lake on OneLake, Direct Lake over mirrored Unity Catalog tables, DirectQuery against a Databricks SQL warehouse, and a composite model with import aggregations. Liping Huang wrote Microsoft's new white paper benchmarking all four side by side, and joins me to walk through the results.

The test runs the same TPC-DS data at roughly 26 million, 260 million and 2.6 billion rows, across cold, warm and hot cache, three filter scenarios and a 20-user load test. No pattern wins everywhere. Direct Lake holds up best for the warm and hot cache reports people open every day, queries that hit an aggregation table stay under 100ms at every volume, DirectQuery catches up (and sometimes wins) at 2.6 billion rows on an X-Large warehouse, and mirrored Unity Catalog tables slow down badly as the data grows.

GUEST BIO (NAME HERE) 👤

Liping Huang is the CEO of Data Leaps and a Data & AI Architect, blogger, YouTuber, keynote speaker and user group organizer (ex-Microsoft, ex-Databricks). Liping works with Tabular Editor as Field CTO for APAC and is passionate about helping Power BI users build better semantic models faster, creating content about Tabular Editor, Power BI, Fabric and Databricks on the Data Leaps YouTube channel and at dataleaps.co.uk.

RELATED CONTENT 🔗

White Paper (PDF)
White Paper Announcement
Data Leaps
Data Leaps YouTube
Liping's LinkedIn

Materialized Lake Views in Microsoft Fabric: How They Actually Work

Video by: Reid Havens

Every report hitting your raw tables runs the same expensive joins and aggregations over and over, even when the underlying data hasn't moved. Materialized Lake Views in Microsoft Fabric pre-compute those results once and store them as Delta tables in OneLake, so consumers read finished numbers instead of recalculating from scratch.

In this video I cover what MLVs actually are, how the automatic refresh logic decides between skip, incremental, and full rebuild, the Spark SQL syntax to create and manage them, and where they sit in a medallion architecture (think of them as a "Gold+" layer).

The part worth sticking around for: point Direct Lake at an MLV instead of your raw tables and you get pre-aggregated data at import speed. In the example here, a 50 million row fact table collapses to around 500K. Fewer rows means faster transcoding into VertiPaq and a lot less DirectQuery fallback risk. That's the single best reason to pair the two.

I also get into when NOT to use them, because they're not free. Sub-minute volatile data, tiny tables, and row-level security needs are all cases where an MLV is the wrong tool.

Comparing Performance Analyzer Data (After Page Optimization)

Video by: Reid Havens

Learn how to interpret and review the Power BI Performance Analyzer data export (JSON). Plus leverage a template I built to let you compare two Performance Analyzer output files to see both the differences in page load time and object (visual) counts. Tune in to learn more!

(Livestream Replay) Lessons learned After Two Years With a Large Model - with Alex Dupler

ABSTRACT 📄

For the past two years, my team has been working on a premium Power BI model that has grown significantly in size, from 50 GB to over 100 GB. During this time, we have gained valuable insights and lessons that we would like to share with you. These include the importance of conducting systematic testing, determining which features and columns will have the most impact on performance, and managing the ongoing refresh of a model of this size. We hope that these tips will be helpful for those looking to optimize and improve their Power BI models.

GUEST BIO 👤

Alex Dupler is a Power BI developer and architect at Microsoft, where he currently serves as the lead Program Manager for a data warehouse platform supporting the company's advertising business. In this role, Alex is responsible for a Power BI import dataset with 90 GB of data. He is also one half of "Two Alex's," a YouTube channel that features live discussions on Power BI topics without one correct answer. Prior to joining Microsoft, Alex worked as a chemist. He is a member of the PBICAT team and has been inactive for a while.

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Alex's LinkedIn