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

(Livestream Replay) Fabric & Power BI: Awesome Use-Cases with DAX Query View - with Zoe Douglas

DESCRIPTION 📄

Join us live as we dive into the world of Microsoft Fabric! In this session, Zoe will walk you through the process of creating semantic models using three powerful approaches: Direct Lake, Import, and DirectQuery (DQ). We'll explore how each of these methods can be utilized within Microsoft Fabric to build robust and scalable models tailored to your data needs.

But that's not all—once we've crafted these models, I'll demonstrate how you can leverage them to write efficient DAX queries using the DAX Query View. Whether you're new to Microsoft Fabric or looking to refine your skills, this demo will provide valuable insights and practical techniques you can apply to your projects.

Bring your questions and curiosity, and let's explore the possibilities together!

GUEST BIO (Zoey Douglas)👤

Zoe is a Product Manager at Microsoft working on Power BI in Microsoft Fabric with features such as DAX query view, Direct Lake, Model Explorer, and Copilot. Before coming to Microsoft I was a Power BI consultant and mentor, enjoying the Power BI community forum and taking on data challenges.

RELATED CONTENT 🔗

Direct Lake
DAX Query View
Web Modeling
Model Explorer

(Livestream Replay) Power BI Storage Modes in Microsoft Fabric - with Phil Seamark

DESCRIPTION 📄

A review of semantic model storage modes available today in Microsoft Fabric. In particular, let's look at the new Direct Lake storage mode and help understand how this compares with Import and Direct Query, and when you might use each.

GUEST BIO 👤

Phil works for Microsoft on the Fabric Customer Advisory Team (CAT) as a DAX and Data Modelling specialist.

As part of this role, he helps global enterprise customers to optimize and tune large data models to perform their best under heavy load.

Phil has over 30 years of experience in I.T. with most of that time building solutions in business intelligence and advanced analytics.

(Livestream Replay) Demystifying Fabric's SQL Architecture & Best Practices - with Bogdan Crivat

DESCRIPTION 📄

Join Microsoft Azure's Corporate Vice-President (VP) Bogdan Crivat and I for this special livestream where we'll do a deep dive into the engineering behind the SQL architecture in Fabric, and how it's both similar and different from the SQL architecture you'd find in various Azure SQL databases. Plus he'll also demo and discuss some best practices for implementation of both Fabric Data Warehouses and Lakehouses. Tune in to learn more!

GUEST BIO 👤

Passioned about advanced analytics, machine learning, in-memory databases and parallel computing. His background is in Mathematics and Computer Science. Bogdan received a degree in Computer Science at University of Bucharest (1997) and a PhD degree from Universitatea Transilvania of Brasov, Romania (2011). He is also the author or co-author of 13 patents (and a number of pending applications), mostly in the areas of data mining, predictive analytics and in-memory analytics.

RELATED CONTENT 🔗

What is Data Warehousing in Fabric?
What is a Fabric Lakehouse?
Bogdan's LinkedIn