What we will cover today

The AI/BI and Genie space (no pun intended) keeps evolving rapidly, and we can only cover a slice of what is really being released. As usual, we tried to summarise the most interesting developments in this post with beautiful visualizations:

• Databricks AI/BI Genie was featured in the Gartner Magic Quadrant

• Genie Code now builds Genie Agents

• Genie Agents can now read unstructured documents (No need for RAG)

• Genie One Persisted Memory

• Gantt Charts in AI/BI Dashboards

• Bookmarks in AI/BI dashboards

• The New Context Engineering Associate Certification

Databricks Secures Best-Ever Debut in Gartner MQ for Analytics and BI

The Analytics and BI landscape is evolving rapidly. As coding agents handle routine manual development (Genie Code can now do entire migrations), human value is shifting toward context engineering, conceptual design, and open architectures.

As consumers of semantic models expand from single BI tools to autonomous AI agents and multi-tool ecosystems, semantic layers must move to where data gravity lives. By combining open-source Unity Catalog Metric Views, governed context in Unity Catalog, Genie, and AI/BI Dashboards, Databricks provides all the foundational building blocks for modern, scalable BI.

Leading research firm Gartner recently validated Databricks' market position in the latest Magic Quadrant. In its debut year, Databricks earned the highest scores ever achieved by a new entrant for both Ability to Execute and Completeness of Vision, securing the second-highest overall position for Completeness of Vision.

Genie Code Now Builds Genie Agents for You

Genie Code can now build and optimize Genie Agents on its own. A single prompt is all it takes to gather the context, wire up the logic, and spin up a working initial version. Watch the magic happen in the demo below:

Beyond initial creation, Genie Code continuously helps refine and iterate on your agents by for example creating benchmark questions. Even better, all of these improvements can be captured as code in a Declarative Automation Bundle (DAB), enabling version control, testing, and automated CI/CD deployment across your environments.

Learn more →

Goodbye Custom RAG: Genie Agents Now Support Unstructured Files

You can now extend your Genie Agent beyond structured data by attaching Unity Catalog Volumes containing unstructured files like PDFs, Word documents, images, and slide decks.

It matters because building AI agents that seamlessly combine structured metrics and unstructured file context used to require complex custom RAG pipelines. Now, Databricks makes it effortless by letting you build hybrid, multi-modal agents in just a few clicks (or a single prompt) while keeping all your data governed under Unity Catalog.

It’s just a click to attach a volume as it can be seen in the screenshot:

Learn more →

Give Genie One a Brain Upgrade 🧠

Re-explaining the same setup instructions to your AI every week gets old fast. Imagine if your friends reset their memory every time you hung out! Thanks to Genie One’s persistent memory, you can now teach it user-level preferences, business rules, or personal context once, and it will remember them across future chats.

Shifted your loyalties from Norway to Spain mid-season because you want to be a winner? Just tell Genie to update its memory, and it adapts instantly. Check out the live memory update in action below:

Learn more →

Native Gantt Charts in AI/BI Dashboards 📊

AI/BI Dashboards now natively support Gantt Charts as a built-in visualization type. They make it simple to track project schedules, group tasks and subtasks, and map start/end dates—giving you a clear, timeline-based view of your operations right alongside your core metrics.

Here is a quick look at how it renders in the UI:

Learn more →

Skip the Filters: Introducing AI/BI Dashboard Bookmarks 🔖

No one likes re-selecting five different drop-down filters every time they open a dashboard. With Bookmarks, users can lock in their favorite combination of filters, parameters, and timeframes to create instant, saved views. It eliminates repetitive setup and makes sharing specific data slices across teams completely frictionless.

Check out bookmarks in action:

Learn more →

Level Up with the New Context Engineering Associate Certification

We spend most of our energy tuning models and writing prompts, but the thing that actually makes or breaks an agent is the context. What it knows, what tools it has access to, and what it remembers. Databricks recently released a certification to address just that: The Certified Context Engineer Associate exam.

The Databricks Certified Context Engineer Associate certification exam assesses an individual's ability to design, assemble, and govern the information that AI agent systems receive at inference time using Databricks.

If you’re building agents on Databricks, this is the cleanest way to show you understand the part everyone underestimates. I’d sit this one.

Learn more →

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So much happened in one month that it is almost hard to keep up. We tried to summarise the most interesting developments in this post:

• Genie Ontology maps and ranks your business’s knowledge

• Genie One has evolved into your agentic coworker

• Customise your Genie One homepage

• Multi-fact relationships and cross-dataset measures

• Reshape visuals on the fly with dashboard variables

• Add Genie visuals to a Databricks dashboard

• DABs (Declarative Automation Bundles) now supports Genie Spaces

• Control Genie’s cost with Unity AI gateway

Genie Ontology maps and ranks your business’s knowledge

Most enterprise AI stumbles for one reason; the context needed to answer a question is scattered across dashboards, queries, pipelines, wikis, tickets, and chat threads. Genie Ontology fixes that. It automatically extracts knowledge from your tables, queries, dashboards, pipelines, and connected apps, then organises it into a living graph of how your company actually works; metric definitions, business terms, custom calculations, and the relationships between them. It even ranks sources using a PageRank-inspired approach (called OntoRank) that weighs authority, usage, freshness, and ties to certified assets. The payoff: 84.5% of questions answered correctly on the first try, and 2× faster than the strongest alternative, making it cheaper in the long-run than generic models.

Learn more →

Genie One has evolved into your agentic coworker

Genie One (f.k.a. Databricks One) began as the entry point for business users to access AI/BI Dashboards, Genie Spaces, and Databricks Apps. It is now becoming the central entry point for every knowledge worker, not just the BI crowd. With per-user authentication it can reach beyond your tables into your email and documents, answering from the sources you already work in and only the ones you personally have access to. It can also run scheduled tasks on its own, like refreshing a metric or sending a recurring summary, and the new custom skills let you teach Genie One reusable, personal actions so you do not have to re-explain the steps each time.

Give it a try: open Genie One in your workspace, connect a source, and hand it a recurring task you would rather not do by hand.

Learn more →

Customise your Genie One homepage

Workspace and account admins can now make Genie One feel like home. Customise the homepage with your brand colors, company logo, a markdown welcome message, and pin recommended Genie Spaces, Apps or dashboards so your team sees the most relevant content the moment they log in. It’s a small change that makes a big difference for adoption especially when onboarding new business users.

What you can customise:

• Company logo

• Brand colour

• Markdown welcome message (links, announcements, etc.)

• Pinned content (Genie Spaces, dashboards, and other assets)

Learn more →

Multi-fact relationships and cross-dataset measures

A star is not enough? Use a galaxy! You can now model more than one fact table in the same dataset and write measures that reach across them. In the example below, Sales and Stock Inventory both join to shared Product and Date dimensions, so a single dashboard can line up units sold against units on hand without stitching the tables together in SQL first. Each fact keeps its own grain on the many side of the relationship, while the conformed dimensions sit on the one side, turning a single star schema into a full galaxy. That means cross-dataset measures like sell-through rate or days of cover come straight from the model.

Learn more →

Custom visualisations with Vega-Lite

You did not find the exact type of visual you want to use in your AI/BI Dashboard? You no longer have to search for the built-in chart types. Custom visualisations let you define your own charts with open Vega-Lite library.

You keep full control over marks, encodings, and styling, while the chart still binds to your governed dataset and reacts to dashboard filters like any other widget.

Learn more →

Reshape visuals on the fly with dashboard variables

Dashboard authors can now define variables that let viewers swap the fields on a visualization without touching the dashboard itself. Point a variable at a set of fields, wire it to a chart’s axis or breakdown, and readers can flip betweenfor example monthlr or yearly granularity from a single dropdown. One dashboard now covers what used to take several near-duplicate charts, and every view stays governed by the same underlying dataset.

Learn more →

DABs (Declarative Automation Bundles) now supports Genie Spaces

You can now define and deploy Genie Spaces as infrastructure-as-code with Declarative Automation Bundles (DABs). The new genie_spaces resource lets you version-control a Genie Space’s title, description, warehouse, data sources, instructions, sample questions, and permissions right alongside your jobs and pipelines. Point it at a serialised geniespace.json file, run databricks bundle deploy, and promote the same space from dev to prod with confidence.

In practice, this makes it much easier to promote a curated Genie experience across environments using the same bundle workflow you already use for jobs, dashboards, and other resources.

# databricks.yml
bundle:
  name: nyc-taxi-genie
  engine: direct # genie_spaces require the direct deployment engine

resources:
  genie_spaces:
    nyc_taxi_genie:
      title: ‘NYC Taxi Trip Analysis’
      description: ‘Ask questions about NYC taxi trip data in natural language’
      warehouse_id: <warehouse-id>
      file_path: ./nyc_taxi_genie.geniespace.json
      permissions:
        - level: CAN_RUN
          group_name: users

⚠️ Requires the direct deployment engine.

Learn more →

Add Genie visuals to a Databricks dashboard

Found an insight in Genie worth keeping? You can now save any visualisation from a Genie Space response straight to a dashboard. Click the dashboard button in the upper-right corner and either create a new dashboard or drop it into an existing one. That helps teams move faster from ad hoc exploration in Genie to reusable, shareable dashboard content without rebuilding the visual from scratch. I’ve been looking forward to this one!

Learn more →

Control Genie’s cost with Unity AI Gateway

As Genie moves to pay-as-you-go billing beyond the free monthly allowance, account admins can now use Unity AI Gateway budgets to monitor and control spend. Budgets can be scoped across accounts, workspaces, groups, or individual users, with shared thresholds, per-user limits, alerts, and usage blocks to help teams scale adoption without losing cost control.

To set it up, an account admin just needs to login to the account console, create a budget under the “Usage” section, and select the databricks-product: genie resource tag. This tracks usage across all Genie products.

Learn more →

Further reading

• Design Beautiful Dashboards in AI/BI

• Databricks Summit Recap (Video)

This month’s updates are squarely focused on:

• Genie Community

• Connect your apps to Genie using MCP Connectors

• One interface to Genie across workspaces

• Dashboard authoring with Genie code

• Replicating Tableau and PowerBI dashboards in AI/BI dashboards

Join the new Genie Community

Databricks has launched the Genie Community, a global, practitioner-driven initiative designed to bring together analysts, data teams, business leaders, and AI enthusiasts who are deploying natural language analytics inside their organizations. The community is community-led and practitioner-driven, organized around a simple mission: Learn Genie. Use Genie. Share Genie. Teach Genie.

For those who go beyond personal use and begin mentoring others, the Genie Geniuses Program provides formal recognition and a pathway to help their organizations build more data-driven cultures. With nearly 500 members already and growing, this is a strong signal that Genie adoption is moving into the mainstream.

Learn More →

Connect your apps to Genie using MCP Connectors

Genie can now reach beyond your Databricks lakehouse to answer questions. With the new MCP (Model Context Protocol) connector support in Genie Chat, users can connect external data sources including Google Drive, SharePoint, GitHub, Glean, Atlassian, and custom MCP servers directly into a conversation.

Once connected, Genie automatically queries these sources alongside your structured data when formulating answers. Each user authenticates individually via OAuth, so there is no token sharing and access controls remain intact.

This matters because a common frustration with AI analytics tools has been their inability to reconcile structured data with the unstructured context living in wikis, documents, and code repositories. MCP connectors begin to close that gap allowing Genie to ground its answers in the full picture of organizational knowledge, not just what lives in tables.

Imagine using Genie to analyse your meeting notes while comparing it to details about your client from Databricks :D

Learn More →

One interface to Genie across workspaces (Account-Level Genie)

Until now, Genie was scoped to a single workspace, a limitation that created friction for organizations operating across multiple Databricks environments. Account-level Genie changes that by providing a unified entry point that surfaces every asset a user is authorized to access, regardless of which workspace it lives in.

The experience centers on a unified homepage that aggregates recently accessed content, trending assets, and personal favourites across the entire account. A cross-workspace search capability lets users find dashboards, Genie Spaces, and Databricks Apps without knowing which workspace houses them.!

Smart recommendations surface relevant assets based on account-wide usage patterns, making it easier to discover work that colleagues have already done.

For enterprises managing dozens of workspaces, this is a meaningful step toward a coherent, organization-wide self-service analytics experience.

Learn More →

Use Genie Code to build a dashboard

Building a dashboard in Databricks AI/BI used to require navigating a series of manual configuration steps selecting datasets, defining visualizations, arranging layouts, and wiring up filters. Genie Code’s new Agent mode automates this workflow end to end.

Simply describe the visual you want in natural language and let Genie Code generate the chart. Then modify the chart using point and click.

Users activate Agent mode within the Genie Code panel, describe what they want, and the agent takes over: searching tables, generating datasets, creating visualizations, configuring multi-page layouts, and applying interactive filters. Throughout the process, the agent pauses to ask clarifying questions or request approval before making significant changes.

The agent also supports context-aware references using @table_name syntax, retains context across steps, and can correct its own errors automatically. For dashboard developers who currently spend significant time on setup rather than analysis, this feature represents a substantial reduction in that overhead.

Learn More →

Import Dashboards from Tableau and Power BI

Migrating from a legacy BI tool has historically meant rebuilding every dashboard from scratch which was a time-consuming process. Databricks now addresses this directly with native import support for Tableau and Power BI files in Genie Code!

Users can upload .twb, .twbx, .tds, .tdsx, and .pbit files directly into a Genie Code chat (up to 100 MB), and the system automatically reconstructs the dashboard in Databricks AI/BI.

The underlying business logic is converted into metric views that mirror the original calculations for measures and dimensions.

The feature is in beta and requires the “Import from External BI to AI/BI” preview flag to be enabled by a workspace administrator. Find out more below.

Learn More →

Subscribe now

Additional Reading

• Genie Community : Join the global practitioner network

• Genie Chat documentation: Full reference for Genie Chat features and MCP connector setup

• Account-level Genie documentation

• AI/BI Dashboard Agent documentation

• Import BI files into Databricks AI/BI

• DatabricksOne has been renamed to Genie

• Embed a Genie Space in your own applications

• Thumbnails for Genie Spaces

• AI-generated comment language selection

• SuperSkills Episode 1: Mastering AI/BI Genie

• Builder mode now default for Measures & Dimensions

• Increased token limit for Genie Agent Mode

DatabricksOne has been renamed to Genie

Have you recently visited your workspace, looked for the Databricks One button and couldn't find it?

Databricks has announced the next generation of Databricks Genie. Genie is now a single chat interface for business users to talk to their data, whether it is Genie Spaces, AI/BI Dashboards, or other trusted assets.

To access Genie, the “New Genie Experience in Databricks One“ Public Preview must be enabled.

No worries, Genie Spaces and Genie Code will still be available in your workspace.

Learn more →

Subscribe now

Embed a Genie Space in your own Applications

You can now embed a Genie space directly inside your own internal applications and portals, the same way you can already embed AI/BI Dashboards.

Below is an example of embedding a Genie space in Confluence.

Learn more →

Thumbnails for Genie Spaces

Genie Spaces are built for business users, so they should be inviting, intuitive, and easy to find. A great thumbnail is the first step to making your space stand out.

AI-generated comment language selection

You can now change the default language for AI Suggested Descriptions. This helps improve accuracy when your business users ask questions in Genie Spaces in languages other than English. Sehr stark!

Learn more →

SuperSkills ep 1: Mastering AI/BI Genie

Our amazing colleagues kicked off a new video series this month — SuperSkills — designed to help both data teams and business users get the most out of the Databricks AI/BI platform.

Episode 1 focuses on Mastering AI/BI Genie: how to configure a Genie space well, how to write effective instructions and sample questions, and how to get consistently accurate answers out of it. If you’ve set up a Genie space and found that it sometimes gives wrong or incomplete answers, this is the video to watch.

It covers the practical side of Genie setup that often gets skipped — things like providing business context, seeding curated SQL, and structuring your metric definitions so Genie can reason over them reliably.

Learn more →

Increased Token Limit for Genie Agent Mode (Public Preview)

Agent mode can now handle significantly longer conversations without losing track of earlier context, thanks to a major increase in the maximum token limit.

This is especially valuable for connected, multi-turn investigations like: “What drove the spike in cancellations last week? Which segments were most affected? How does that compare to last year?” Agent mode can now hold all of that context together, enabling longer reasoning chains, more data explored per session, and better coherence across a full analytical workflow.

Learn more →

Builder Mode Now Default for Measures & Dimensions (Public Preview)

When creating measures and dimensions in a Genie Space, you now land in Builder mode by default rather than a blank YAML editor.

Builder mode gives you a form-based interface to define your metric: pick the base table, choose an aggregation, set filters and add a description. The correct YAML is generated for you in the background, and you can still switch to the raw YAML view if you prefer. This lowers the barrier for data teams who want to get metrics defined quickly without memorising the metric schema, and makes the experience more approachable for analytics engineers who are new to the platform.

Especially the AI-assisted measure creation makes it possible for non-technical users to define business logic without writing any code.

Learn more →

That’s it for this month. As always, reach out to your Databricks account team for anything in private preview, and hit reply if there’s a topic you’d like covered in a future issue.

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• Databricks One mobile

• (Unified) Chat in Databricks One now in beta

• Unity Catalog Metric Views in Excel, Google Sheets & PowerBI

• New external dashboard embedding options

• Genie Agent Mode now in Public Preview

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Databricks One Mobile

Databricks One Mobile brings the new Genie multi-agent chat experience to iOS and Android, enabling business users to securely access and interact with their data from anywhere.

With Databricks One Mobile, users can ask Genie questions, explore AI/BI Dashboards, and access Databricks Apps from their phone. It gives business users a simple way to analyze data and make decisions while on the go.

Reach out to your Databricks account team to try this out.

(Unified) Chat in Databricks One

Databricks One now has a dedicated unified Chat interface, a full-screen, natural language experience that goes beyond a simple Genie space. When you ask a question, Chat first looks for a relevant Genie space to route your question to. If it finds one, it uses it. If not, it falls back to Genie Agent mode to search across your available data assets and answer anyway.

The practical benefit: a business user no longer needs to know which Genie space covers which data domain. They just ask, and Chat figures out where to go. Think of it as a “unified front door” to all your curated analytics one place to ask “What were our top-performing regions last quarter?” without worrying about whether that lives in the Sales Genie space or a Marketing Genie space.

Learn more →

Read UC Metric Views/Business Semantics from Excel, Google Sheets & PowerBI

You can now connect to Unity Catalog metric views directly from Microsoft Excel, Google Sheets and PowerBI, bringing governed, semantically consistent metrics into the tools your business users already live in.

Instead of exporting a static CSV or asking an analyst to pull data, a finance user can open their spreadsheet, connect to the Databricks Excel Add-in or Google Sheets connector, and pull in metric views like “Monthly Recurring Revenue” or “Active Users” with consistent definitions enforced at the source. No SQL required. And because the data is live from the Lakehouse, the numbers they’re working with are always up to date and trusted.

Try out the Excel connector , Google Sheet or PowerBI Connector

New Dashboard Embedding Options

Two big updates shipped in the the last week for teams that want to embed Databricks dashboards inside their own applications or portals:

Embedding for External Users (formerly “embedding with app delegated authentication”) is now in Ungated Public Preview and rolling out to all workspaces over the next week. This lets you embed dashboards for users who don’t have a Databricks login. For example, customer-facing portals or partner reporting tools where you want to surface your Lakehouse data without requiring end-users to authenticate with Databricks directly.

Embedding with SSO is now in Private Preview. This option allows your existing corporate SSO identity to flow through into embedded dashboards, so employees accessing an embedded dashboard inside an internal app are automatically authenticated using your organisation’s identity provider, no separate Databricks login needed. Reach out to your Databricks account team to try this out.

Learn more →

Genie Agent Mode now in Public Preview

Genie’s Research Agent mode, which we covered in the January release, has moved into Public Preview! Where a standard Genie question returns a single query result, Agent mode breaks down a question like “Why did churn increase in Q3, and which customer segments drove it?” into multiple steps, runs them in sequence, and returns a structured narrative report.

This is particularly useful for analysts who want a first-pass investigation done automatically before diving deeper themselves, or for business users who need a well-reasoned answer, not just a number.

Learn more →

• Prompt-Driven Dashboards with Agent Mode

• Building Metric Views Without Code

• Measuring AI/BI Adoption

• Genie Spaces: From Prototype to Production

• Enterprise User Provisioning for Genie

• Additional Reading

Prompt-Driven Dashboards with Agent Mode

In the past, crafting dashboards required days or even weeks of effort. Despite this investment, many dashboards were only used for ad hoc analysis and struggled to achieve broad adoption. Additionally, dashboard creators often became bottlenecks whenever new visualizations or changes were needed.

With the new Agent Mode in Databricks AI/BI, this paradigm shifts entirely. Dashboards can now be generated and refined within minutes using natural language prompts.

In the YouTube video, you’ll see how World Cup soccer results are analyzed in just two minutes — showcasing how conversational analytics dramatically accelerates insight generation.

Genie Spaces: From Prototype to Production

To successfully operationalize Genie, organizations need a structured approach that combines strong metric modeling, governance, access control, and clear ownership. The Databricks blog provides a practical framework for making Genie Spaces production-ready — helping teams move beyond experimentation and build trusted, scalable agentic BI systems.

Learn More →

Building Metric Views Without Code

Metric Views are among the most critical objects in Unity Catalog in the era of Agentic BI. They provide a governed, centralized semantic layer that serves as a single source of truth for both AI agents and human users to understand and interpret business metrics consistently.

In the past, defining Metric Views required working with YAML files, which made adoption challenging for many business users

With the introduction of the new UI, this barrier has been significantly reduced. The interface is context-aware of the underlying data model, accelerating not only data modeling but also the definition and governance of metrics across the organization.

Enterprise User Provisioning for AI/BI

In the past, granting access to AI/BI was not frictionless, as users had to be manually provisioned from Azure Entra ID into Databricks. This process created administrative overhead and slowed down broader adoption among business users.

With the release of Automatic Identity Management, this has fundamentally changed. Users and groups from Azure Entra ID can now be synchronized to Databricks without the need to deploy and manage a dedicated Enterprise Application for SCIM. As a result, administrative effort has been significantly reduced, and organizations can onboard business users to AI/BI capabilities much faster and at scale.

If adding business users to Databricks AI/BI feels slower than it should, it may be time to rethink your approach. Read the blog post and take the next step toward frictionless AI/BI enablement.

Learn More →

Additional Reading

• Vote for this feature request of a native integration of Databricks Metric Views into Power BI

• Participate in the upcoming webinar, “Leave Legacy BI Behind“ co-hosted by Anthropic, Databricks and Alex the Analyst and build your first end-to-end agentic analytics workflow.

• Learn how to parameterize your AI/BI Dashboards for deployment based on the blog article from the one and only Hubert Dudek

• Dashboards now support a more flexible layout with a denser 12-column grid (up from 6), giving you finer control over widget placement so you can align content precisely and fit more information into a single view. Read more here.

• Read Databricks blog post “What’s New in AI/BI - February 2026 Roundup“

Hi there!

Welcome to the Databricks AI/BI Lakehouse; your monthly pulse on the latest features, tips, and real-world examples that help you turn data into insights faster.

Each edition will highlight key updates, showcase practical use cases, and direct you to deep dives, enabling you to stay current and maximise the benefits of AIBI dashboards & Genie across your teams.

Looking forward to sharing what’s new and hearing your feedback!

What we will cover today.

• Answer the “why” questions with Genie Research Agent Mode

• You can now talk to your dashboards with Genie-in-Dashboards

• Embedded Your Dashboards outside Databricks

• Send dashboard snapshots directly to Slack or Teams

• Create forecasts directly in your dashboard

Answer the “why” questions with Genie Research Agent Mode

Genie Research Agent mode is a new capability that lets Genie answer complex, multi-step analytical questions, not just simple, single-query prompts. Instead of generating one SQL statement, it creates a research plan, runs multiple queries, analyzes intermediate results, and combines them into a richer, more explanatory answer. This makes it ideal for “why” and “how” questions such as identifying the drivers behind revenue changes, customer churn, or campaign performance.

Because it performs iterative reasoning, Research Agent Mode can surface deeper insights with tables, charts, and citations to the queries it used. It works best when your Genie space has clean metadata and clear business context.

Learn More →

You can now talk to your dashboards with Genie-in-Dashboards

Genie-in-Dashboards brings conversational analytics directly into the Databricks AIBI dashboard. Instead of relying solely on pre-built visuals, the Ask Genie button on published dashboards enables viewers to ask questions in natural language and receive answers based on what they see, without needing to navigate away and lose context.

Learn More →

Embedded Your Dashboards Outside Databricks

Embedded Dashboards let you take AI/BI dashboards and securely place them inside external applications such as customer portals, internal tools, or partner-facing products. This provides end users with an interactive analytics experience without requiring them to log into Databricks. You control exactly which dashboards they see and what data they can access, while the dashboard remains fully powered by your Databricks backend.

This feature is ideal for productizing analytics: SaaS providers can deliver insights to customers, enterprises can enrich internal apps with real-time metrics, and teams can share data visuals seamlessly across systems.

Learn More →

Send dashboard snapshots directly to Slack or Teams

Scheduling dashboard updates ensures that your dashboards display up-to-date information and improves performance for end users.

Subscriptions keep stakeholders informed by sending dashboard snapshots to email, Teams or Slack.
Learn more →

Create forecasts directly in your dashboard

Using the built-in AI Forecast functionality, you can apply predictive forecasting to line charts to visualize future trends and patterns.

Your line chart must have a temporal date field on the x-axis and a single numeric field on the y-axis.
Learn More →

We hope you found this beneficial and we look forward to seeing you in the next one!

Databricks AI/BI is developing fast! Every week there are new announcements and releases that we can’t wait to get our hands on. Here is a subset of the features that we think you would find interesting.

What we will cover today

• Databricks One is Generally Available (GA)

• Consumer access is default for all workspaces

• Genie is now embedded in Unity Catalog lineage

• Value sampling features renamed

• You can now view a sample of your data in Genie spaces

• Download a PDF export of Genie’s research agent mode reports

Databricks One is Generally Available

Databricks One is now generally available, bringing a simplified, business‑friendly experience for working with data, dashboards, Genie, and apps. Instead of juggling multiple interfaces, business users land in a single, cohesive UI where they can browse content, run dashboards, and ask natural language questions. They can also access Databricks apps which they may have access to.

Learn more →

Consumer access by default (Public Preview) – business‑user

Now that Databricks One is GA, workspaces can now be configured so that new users receive “consumer access” by default, reflecting a “business‑user‑first” stance. Think about the consumer access role as a business-user role; users who want to consume data and get insights from already curated datasets.

Instead of giving everyone authoring privileges from day one, most users land in a safe, view‑only environment where they can use Databricks One, interact with dashboards, and explore Genie spaces without risking accidental changes. This greatly simplifies onboarding at scale, since admins can separate “builders” from “consumers” using groups and entitlements rather than ad‑hoc permissions. It also aligns Databricks more closely with traditional BI governance models while keeping AI/BI capabilities front and center for business users.

Learn More →

Unity Catalog lineage for Genie spaces

You can now view Genie spaces in Unity Catalog lineage, making it much easier to understand how conversational analytics depend on underlying data assets. This means data teams get a complete view of which tables, views, and downstream dashboards a Genie space is related to, and can assess impact before making schema changes. In practice, this helps with audits, impact analysis, and debugging odd answers that might be caused by upstream data changes.

Learn more →

Value sampling features renamed

The “value sampling” (now called “prompt matching”) capabilities in Genie have been renamed to better reflect what they actually do, without changing their underlying functionality. Prompt matching is the overall functionality that allows Genie to match columns and values that are most relevant to the user's question, and correct spelling issues in user prompts.

• Prompt matching, formally called value sampling

• Format assistance, formally called example values: provides representative values for all eligible columns, helping Genie understand data types and formatting patterns.

• Entity matching, formally called value dictionaries: provides curated lists of distinct values for up to 120 columns where users are likely to reference specific entries, such as states and product categories

The net result is the same smarter Genie, but with labels that are easier to explain to stakeholders.

Learn more →

View sample data Genie has access to

Genie spaces now expose a dedicated sample data view so authors and space managers can quickly see what Genie actually “sees” in the underlying tables. This makes it much easier to validate that the right columns, value distributions, and filters are in place before rolling a space out to a wider audience. As a practical benefit, you can debug confusing answers by checking whether Genie’s sample includes representative values or whether key records are missing. It also helps during onboarding: new authors can orient themselves on a space’s data context without writing SQL.

Learn more →

Research Agent mode export to PDF

For teams using Research Agent mode in Genie, you can now export reports directly to PDF. This turns multi‑step, AI‑generated analyses into shareable artifacts that can be attached to emails, tickets, or documentation systems without additional formatting work. It’s particularly useful for executive briefings and recurring analyses, where you want a consistent, polished output that captures both narrative and supporting tables.

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If you still have appetite for me, check out our previous announcements or the most recent release notes. See you next month!