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.

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Model Your Data in Power BI: Star Schemas, Filter Flow & Field Lists

Video by: Reid Havens

Plenty of Power BI issues that look like DAX issues start one layer down, in the model. The formula is correct, the relationship underneath it isn't doing what you assumed, and you spend an afternoon rewriting a measure that was never the problem.

This one covers the foundation the rest of it sits on. Why a model beats one giant flat table (smaller storage, better compression, faster refresh, and filter paths you get without writing any code), how dimensions and facts split the who-what-when-where from the numeric events, and what actually happens when someone picks a value in a slicer and that filter travels down the relationship into the fact table.

Then star versus snowflake, compared on the four things that decide it in practice: query performance, DirectQuery latency, how long the DAX ends up, and whether business users can find a field without asking someone. Star wins on all four. I also get into why the snowflake version keeps turning up anyway, since it usually arrives from a source system rather than from a decision anyone made.

The last section is the one that gets skipped. Every visual in Power BI generates a query with three ingredients (filter, group, summarize), and once that clicks, a wrong number stops being mysterious. After that it's the fields list itself: friendly names instead of cgo_key_fk, display folders, hidden keys, and descriptions that surface as tooltips. It sounds cosmetic, but an unreadable fields list is how you end up with six competing Excel copies of the same report.

Demystifying Databricks AI Coworker (w/ Youssef Mrini)

LIVESTREAM DATE/TIME 📅

September 11th - 9:30 AM (Pacific Time)

DESCRIPTION 📄

As enterprise AI transitions from basic conversational interfaces to context-aware execution, AI coworkers are redefining how organizations interact with their data ecosystem.

In this session, we demystify the paradigm of Databricks AI Coworker and examine how agentic workflows adapt to diverse operational needs, from plain-language business automation and decision support to code-first data engineering and developer pipelines.

GUEST BIO (Youssef Mrini) 👤

Youssef Mrini is a Developer Advocate at Databricks focused on empowering teams to build on the Databricks Data+AI Platform. His work centers on the Databricks Ecosystem such as AI/BI, Unity Catalog, Delta Lake, and native agentic workflows like Genie. Youssef produces deep-dive technical tutorials, and production-ready guidance dedicated entirely to maximizing value on Databricks.

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Modern DAX in Power BI: UDFs, Window Functions, INFO & More (Part 4)

Video by: Reid Havens

DAX has shipped more new surface area since 2022 than in the five years before it, and most of that never made it into anyone's habits. Part 4 of the series covers the five additions that change how you actually write code, where each one earns its keep, and where it still falls short.

User-defined functions are the headline. Reusable, parameterized DAX stored as a model object, so the pattern you keep copying between six measures finally lives in one place. We build one, call it with and without default parameters, and get into the limitations that are still there in the current release.

From there it's window functions (OFFSET, INDEX, WINDOW, RANK, ROWNUMBER) for row-relative math that time intelligence can't reach, INFO functions for querying your own model metadata as a table, EVALUATEANDLOG for debugging without littering the model with temporary measures, and dynamic format strings that follow context instead of sitting fixed on the measure.

The last stretch runs through the smaller additions that still come up: TREATAS, GROUPBY, DETAILROWS, ERROR, and TOCSV/TOJSON. None of them need a section of their own, but skipping them leaves gaps.

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DAX Modern Guide (Series Part 4)