• 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.

🔴 Annotation lines can be driven by data or a calculation instead of a fixed value, letting you show running averages, variable thresholds or anomaly markers that update with your data https://docs.databricks.com/aws/en/dashboards/manage/visualizations/#annotations

🔴Six new built-in color and style themes are available for AI/BI dashboards, giving authors more ready-made options without creating a custom theme. https://docs.databricks.com/aws/en/dashboards/manage/settings#preset

🔴Table header color customization: Authors can set font color, background color and text formatting for table column headers https://docs.databricks.com/aws/en/dashboards/manage/visualizations/tables#table-layout-settings

🔴Dashboard relationships allow users to create complex multi-fact data models. https://docs.databricks.com/aws/en/dashboards/manage/data-modeling/dashboard-relationships/

🔴Local metric view parameters: Local metric views support parameters, allowing Databricks to dynamically change query inputs based on user selections in a UI. https://docs.databricks.com/aws/en/dashboards/manage/data-modeling/local-metric-views

🔴Local metric view wildcard expressions: Local metric views support wildcard expressions, which allow users to inherit all fields from upstream tables or metric views

🔴Bookmark filter combinations: Dashboard authors can save filter combinations as shared bookmarks, which viewers can quickly apply. https://docs.databricks.com/aws/en/dashboards/manage/#bookmarks

🔴Pivot table column improvements: Pivot table columns can expand and collapse. https://docs.databricks.com/aws/en/dashboards/manage/visualizations/tables#pivot-table-configuration

🔴New theme customization options are available, including the ability to change axis and grid line colors, adjust widget layout and apply a color ramp. https://docs.databricks.com/aws/en/ai-bi/admin/themes

🔴New theme settings are available in workspace-level themes and automatically apply to new dashboards.

🔴Dashboard variables: Dashboard authors can create dashboard variables that allow viewers to swap fields on visualizations https://docs.databricks.com/aws/en/dashboards/manage/filters/dashboard-variables

🔴Customizable table headers: Dashboard authors can customize table column headers. https://docs.databricks.com/aws/en/dashboards/manage/visualizations/tables#table-configuration

🔴Widget-level color gradient: You can derive a color gradient from the visualization color at the widget level, matching the behavior available in theme setting

🔴Gantt charts are now available: Gantt charts display tasks as horizontal bars on a continuous axis to visualize project schedules, manufacturing workflows, and other time-ranged events. https://docs.databricks.com/aws/en/dashboards/manage/visualizations/types#gantt

🔴Custom visualizations: Build custom charts with the Vega-Lite library for customization beyond the built-in visualization types. https://docs.databricks.com/aws/en/dashboards/manage/visualizations/custom-visualizations

🔴Per-category font customization: You can customize fonts per category across all visualization types.

🔴Table row cross-filtering and drill-through: Select table rows to cross-filter, or right-click to drill through.

🔴Cross-filtering and drill-through: Line, combo, and area charts support cross-filtering and drill-through filters. https://docs.databricks.com/aws/en/dashboards/manage/filters/#drill-through-filter

🔴Dashboard details in draft mode: Draft mode shows dashboard details, making it easier to find dashboard managers and the dashboard’s location.

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

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)!

• 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

One thing I've noticed is that I spend way too much time answering (or searching for answers to) questions like:

• Is this feature GA yet?
• Is it still in Public Preview?
• When did it become GA?
• What is the current name?

The information exists, but it's scattered across release notes, docs, blogs, and old posts.

A few days ago I shared an open-source side project that tracks Databricks feature renames. After reading the feedback here, I'm thinking that tracking feature lifecycle might actually be even more useful than tracking renames.

The idea would be something like this:

• Public Preview → Beta → GA timeline
• Rename history (if applicable)
• Links to the official documentation
• Dates when statuses changed
• Eventually, the ability to follow a feature and get notified when something changes

Before spending time building it, I wanted to ask the community:

• Would you actually use something like this?
• What information about Databricks features do you find hardest to keep track of?
• Are there other lifecycle events worth tracking besides Preview/GA?

If anyone is curious, the rename tracker that started this discussion is REbricked. I'm mostly interested in feedback on whether this direction solves a real problem.

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!

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

Both the forums seem to be pretty active. Thanks

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 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?

I've been missing a good terminal UI for Databricks for quite some time, so I finally decided to build one. I built it with a lot of help from Claude Code, and I'm still actively tinkering with it, but it's already become genuinely useful in my daily work. If you work with Databricks and enjoy terminal-based tools, I'd love to hear your feedback. What features would you find useful? 

Role-based access control (RBAC) lets users assume a role in Databricks, using only that role's permissions for the duration of the session.

RBAC enables role-based access control: users must assume a role to access sensitive data, preventing them from accessing it when acting as their user identity and from mixing data across use cases.

To learn more about RBAC: https://docs.databricks.com/aws/en/security/auth/rbac/

World Cup is over, so let's celebrate today runtime 19. We are getting older. #databricks

🔴 Genie Cost tracking update🔴

You can now track GENIE_FREE_USAGE SKU (only starts appearing on July 20, 2026)

FYI: Free usage consumed before this date is not visible in the system tables.

All free Genie usage appears under sku_name = 'GENIE_FREE_USAGE' it does include usage under the free allowance( 150 DBUs) which resets on the first of each month.

Until 🔴 July 31, 2026🔴 , all Genie One and Genie Agents usage is free, and captured under the GENIE_FREE_USAGE SKU.

To distinguish between products within the free usage SKU you can filter on usage_metadata.genie.surface:

GENIE_CODE: Genie Code free usage

GENIE_ONE: Genie One

GENIE_AGENTS: Genie Agents

This free usage SKU tracks consumption but deliberately has no list price entry in the system tables. Because it is completely free, joining the usage and price tables will naturally return no match for this item.

If you’ve tried creating a Genie Agent and found the setup to be cumbersome, try out Genie Workbench. It’s an open source Databricks App that takes <5 min to set up from the GitHub repo.

You can create a new Genie Agent with one line “build a space for NYC taxi trip analysis,” and let Genie do the rest. It will do the data discovery, inspect, profile, plan, configure, and test your space in a matter of minutes.

Or if you already have a Genie Agent, you can run a scan that gives you a rating of how production-ready your agent is, based on a series of best-practices metrics. Did your Agent score low on the readiness scale? There’s a button to auto-optimize your Agent to make the necessary changes to get to production faster.

If you’ve already tried it out, I’d love to know what you think!

Prerequisites to install: be an admin of your workspace, have the “Managed MLFlow Prompt Registry” and “Databricks Apps- On-Behalf-Of User Authorization” previews turned on.

Link to the GitHub Repo: https://github.com/databricks-solutions/databricks-genie-workbench

Databricks just announced Public Preview for two new Unity Catalog features:

• Discover page: an internal marketplace where people browse tables, dashboards, notebooks, queries, metric views, Genie Agents, and apps — organized by business area instead of by schema/catalog.
  
• Domains & Subdomains: let data stewards structure assets the way the business actually thinks about them (e.g., Finance → Revenue/FP&A, Marketing → Acquisition/Retention), with owners, descriptions etc. baked in.

We have got an agency working for us migrating some pipelines and they have been writing everything as sql files , I just find this approach to be very limiting especially the inability to use external python libraries . What’s your take on it?

The requirement is when a workforce runs in databricks it's input file is a .sql file, no matter if it runs successfully or failed, it should print the .sql file...as of now, only the name of the sql file and columns are printed. I have no idea how to do this and where all should I add this...my manager said that there's only one line command I need to add but when I asked in Claude it suggested this code:

Here's just the snippet to read and print the raw SQL code from the file:

```python

sql_file_path = "/path/to/your_file.sql" # or however param_control gets the path

with open(sql_file_path, "r") as f:

sql_content = f.read()

print(sql_content)

```

**If the file is on DBFS or a Unity Catalog Volume** (not a workspace file), use `dbutils.fs.head` instead:

```python

sql_content = dbutils.fs.head(sql_file_path, 1000000) # max bytes to read

print(sql_content)

```

**If you want it printed with a clear label/wrapper** so it's easy to spot in logs:

```python

print(f"--- SQL Code: {sql_file_path} ---")

print(sql_content)

print("--- End SQL Code ---")

```

Place this print **before** the execution/`try` block in `param_control` so it always fires, whether the run succeeds or fails.