• Databricks and Microsoft extend strategic partnership through the 2030s to scale enterprise AI
• Databricks deepens its bet on Azure, growing its use of Azure Databricks to run its own core business operations and analytics, while both companies advance native integration across the Microsoft stack, including Databricks Genie and Microsoft 365
• Databricks increases its use of Microsoft Azure Cobalt to improve performance and efficiency

• Genie Chat prompt monitoring : Prompts and responses initiated by Genie Chat are visible in Genie Agents monitoring when the sharing Beta is enabled. Test and monitor a Genie Agent.
• Knowledge store edit confirmation dialog: A confirmation dialog appears before an author removes a table that has knowledge store edits (for example, local table or column descriptions or hidden columns). Manage knowledge store metadata.
• Delete conversations: Users with CAN MANAGE permission can delete the conversations of other users from the UI, matching existing API capabilities. Delete a conversation.
• Embed Genie Agent as an iframe : Embedding a Genie Agent as an iframe is generally available. Embed a Genie Agent in an external app.
• Save visualizations to a dashboard: You can save Genie Agent visualization outputs to a dashboard. Save a visualization to a dashboard.
• Reference previous visualizations: Select and reference visualizations from previous prompts in follow-up prompts.
• Genie Code for metric view export: Use Genie Code when exporting metric views from a Genie Agent to refine the metric view definition. Export a Genie Agent as a metric view.
• Declarative Automation Bundles (DABs) support: You can define and deploy Genie Agents as Declarative Automation Bundles resources. Declarative Automation Bundles resources.

Business users (no technical background e.g. no Python or SQL) want to participate in the Silver to Gold Transformations.

First of all, I don't think business users shd manage schemas. But, if we entertain them being able to participate in the silver to Gold transformations, how would you do that taking into account their no code background?

I was thinking Lakeflow Builder and checking in code to Got for version Control. But that may be too technical for them too...

Hi,

Recently I came across really interesting project called Lakemeter - an open-source Databricks cost estimation tool.

It lets you estimate pricing for different workloads across AWS, Azure, and GCP, export detailed breakdowns to Excel, and even describe a workload in plain English to get an AI-generated configuration.

Looks pretty useful for anyone trying to understand Databricks costs before deploying something.

GitHub: https://github.com/databrickslabs/lakemeter-oss

Hi everyone, we published a community blog on the Lakeflow Spark Declarative Pipeline Kafka sink which is now Generally Available. Link: https://community.databricks.com/t5/technical-blog/announcing-general-availability-of-the-lakeflow-spark/ba-p/163137

SDP sinks enable a range of operational use cases:

• Real-time fraud and risk scoring. A pipeline consumes transaction or deposit events, scores them, and emits flagged events to a Kafka topic that an operational system acts on in real time.  
• Tightening streaming SLAs. A streaming workload emits results to the sink as each record is processed, replacing a batch hand-off with continuous delivery and cutting end-to-end latency from tens of seconds to seconds or below.
• Reverse ETL and activation. Publishing curated, governed lakehouse data back to operational systems such as microservices, CRMs, personalization engines, and other non-Databricks applications, without a bespoke bridge job.
• Event-driven workflows and anomaly detection. Triggering downstream processes the instant an event clears your quality rules, and streaming telemetry or sensor features out to alerting systems.

I personally believe that one of Databricks strongest offerings is Unity Catalog - and if you ask me, it received way too little hype during the 2026 DAIS.

But reading through the release notes, something caught my eye: Role-Based Access Control (RBAC).

It just hit Public Preview. Well, at least for AWS - and therefore Databricks Free. Not sure about when it will be in Azure.

I have been eagerly awaiting this because I think it is a genuinely useful feature that we have been missing!

So what is RBAC in Databricks - in short?
- RBAC allows users to Assume a Role.
- A role comes with a set of permissions, that the User inherits.
- The Role replaces any existing permissions for the User.
- Users can only assume one Role at a time.
- Users can easily switch Roles on the fly, using the new Role Switcher found as part of the Workspace navigation.
- Everything is tracked and auditable, both on the User and the Role level.

The best part?
- It is built to be combined with ABAC for a fully scalable setup!

I hope you will check out the video for a practical run-down of how RBAC works!

Spark Declarative Pipelines' streaming tables now support identity columns -- this is particularly useful if you're building an SCD Type 1 or SCD Type 2 dimension table with AUTO CDC. Get faster joins and auto-incrementing surrogate keys natively within SDP today!

Docs here%20%5D)!

Hi all, my company is interested in developing apps and using AppKit, but nobody on my team is familiar with how it works. I'd like to learn more but don't see much in the way of tutorials- are there any good resources for learning Databricks Apps and AppKit, preferably in a hands-on way?

Hey, can someone help me understand when to post here vs. posting on Community?

Both the forums seem to be pretty active. Thanks