Databricks Data + AI Summit 2026 – Recap & Update
It’s been a couple of weeks since the announcements at DAIS 2026, and I wanted to give a recap of the most exciting new Databricks features, and give an update on what has changed since the Summit wrapped.
Building a Secure Foundation for Azure Databricks: Lessons from Designing a Secure Reference Architecture
In today’s digital environment, it’s no longer enough for businesses to simply have an app. Users want software available to them anytime, and anywhere. Whether that means using a phone while traveling, a tablet in the field, a desktop computer in the office, or a web browser at home. This shift has made cross-platform development […]
Tuning a Genie Space until analysts trusted it: how Spaces ended up coexisting with Power BI
As a Data Engineer, I spend far more time working as the glue between systems than actually implementing new logic. Business wants a question answered. Analysts go check the semantic layer to see if the answer already exists, and a lot of the time it doesn’t, let alone exists as a Power BI dashboard somewhere.
Auto Loader vs COPY INTO in Databricks: A Decision Guide for Data Engineers
In modern lakehouse pipelines, one of the first ingestion decisions data engineers face is whether to use Auto Loader or COPY INTO. Both can load files incrementally into Delta tables, but they solve very different operational problems. Choosing the wrong one can impact scalability, cost, reliability, and maintenance overhead.
A Field Study: Databricks Genie vs Microsoft Fabric
Recently I have been leading an assessment for a global industrial manufacturer comparing Databricks Genie against an already mature Microsoft Power BI/Fabric environment. Genie feels like an up-and-coming platform gaining significant momentum alongside more established incumbents.
Databricks Logs Explained: Where to Look When Things Break From Driver to Delta
A Databricks job fails… or worse, it runs but performs poorly. You open the workspace and face a familiar question: Where do you start? Driver logs? Spark UI? Executor logs? Query history? Without a clear approach, it’s easy to jump between tabs and waste time chasing symptoms instead of root causes.
Lakehouse Accelerator – Wisconsin Databricks User Group
Data Engineering Panel – Wisconsin Databricks User Group
Building a Scalable Geospatial Heatmap App on Databricks
A client came to us with thousands of services spread across every ZIP code in the United States. They wanted to see what areas had plenty of coverage and what areas were underserved. Static tables and dashboards weren’t good enough. They wanted something fast, interactive, and geographic that would allow them to explore where their […]
Data Scientist Panel – WI Databricks User Group
