Databricks

What shipped

• Genie and AI/BI experiences: Providers can now share a Genie Space with users outside their organization using OpenSharing. (17 Jun)
• Governance and compliance controls: Rolling out starting June 22, the statement_text column in system.query.history returns for users who are not account admins or members of the databricks_pii_access account-level group. (17 Jun)
• Agentic development workflows: Omnigent is now available in Beta. Omnigent is a coding agent meta-harness that wraps coding agents such as Claude Code and Codex with a common platform: a web UI, persistent and shareable sessions, team… (17 Jun)
• Lakeflow pipeline operations: Lakeflow Designer will be available by default for workspaces with the compliance security profile enabled in late July 2026. (16 Jun)
• Lakeflow pipeline operations: Lakeflow Designer is now generally available. Lakeflow Designer is a visual, no-code experience for preparing and transforming data on a drag-and-drop canvas, with all workflows backed by production-ready… (16 Jun)
• Genie and AI/BI experiences: You can now connect Genie to Microsoft Copilot Cowork through the Genie managed MCP server, so users can ask natural-language questions about their Azure Databricks data without leaving Copilot Cowork. (16 Jun)
• Marketplace and app extensibility: You can now discover, install, and run third-party data and AI applications from Databricks Marketplace directly in your own Unity Catalog-governed workspace. (16 Jun)
• Secure data sharing: Azure Databricks will soon charge providers using OpenSharing SecureConnect for cross-region and public-internet data egress. (15 Jun)
• Genie and AI/BI experiences: You can now use the Azure Databricks Genie mobile app to access Genie One from iOS and Android devices. (15 Jun)
• Genie and AI/BI experiences: Chat in Genie One (GA) : Chat in Genie One is now generally available. Chat provides a unified interface for asking data questions in natural language using your Genie Spaces, dashboards, queries, and metric… (15 Jun)
• Databricks Runtime 19: June 15, 2026: Databricks Runtime 19 is now available in Beta, powered by Apache Spark 4.2.0. Behavioral changes JDK 17 removed : Databricks Runtime 19 only supports JDK 21. (15 Jun)
• Databricks Runtime 19 and Databricks Runtime 19 for Machine Learning are in Beta: Databricks Runtime 19 and Databricks Runtime 19 for Machine Learning are now in Beta, powered by Apache Spark 4.2.0. (15 Jun)

Why it matters

• Genie and AI/BI experiences: Databricks is expanding conversational analytics and workspace-native AI experiences for business users.
• Governance and compliance controls: The platform is tightening controls around sensitive metadata, audit trails and compliance-profile behaviour.
• Agentic development workflows: Databricks is adding more agent and Copilot-adjacent tooling around coding, analytics and workspace productivity.
• Lakeflow pipeline operations: Lakeflow updates continue the push toward lower-friction data engineering and managed pipeline design.
• Lakeflow pipeline operations: Lakeflow updates continue the push toward lower-friction data engineering and managed pipeline design.
• Genie and AI/BI experiences: Databricks is expanding conversational analytics and workspace-native AI experiences for business users.
• Marketplace and app extensibility: Databricks is making more partner and third-party functionality available directly inside the platform.
• Secure data sharing: Sharing updates point to more governed external collaboration around data products and AI/BI assets.
• Genie and AI/BI experiences: Databricks is expanding conversational analytics and workspace-native AI experiences for business users.
• Genie and AI/BI experiences: Databricks is expanding conversational analytics and workspace-native AI experiences for business users.
• Databricks Runtime 19: June 15, 2026: Databricks Runtime 19 is now available in Beta, powered by Apache Spark 4.2.0. Behavioral changes JDK 17 removed : Databricks Runtime 19 only supports JDK 21.
• Databricks Runtime 19 and Databricks Runtime 19 for Machine Learning are in Beta: Databricks Runtime 19 and Databricks Runtime 19 for Machine Learning are now in Beta, powered by Apache Spark 4.2.0.

How data teams could use it

• Genie and AI/BI experiences: Analytics teams can test where Genie reduces ad hoc report queues while keeping governed metrics and permissions in place.
• Governance and compliance controls: Security teams should check whether monitoring, troubleshooting or audit workflows need adjusting as redaction and compliance defaults change.
• Agentic development workflows: Engineering leads can trial these features on low-risk workflows and define review gates before allowing production code or pipeline changes.
• Lakeflow pipeline operations: Data teams can assess whether visual design, compliance-profile support or GA readiness changes the build-vs-code path for new pipelines.
• Lakeflow pipeline operations: Data teams can assess whether visual design, compliance-profile support or GA readiness changes the build-vs-code path for new pipelines.
• Genie and AI/BI experiences: Analytics teams can test where Genie reduces ad hoc report queues while keeping governed metrics and permissions in place.
• Marketplace and app extensibility: Platform owners should review app install controls, procurement flow and security review before enabling broad adoption.
• Secure data sharing: Teams with partner analytics or customer-facing data products should review billing, access and external-sharing controls.
• Genie and AI/BI experiences: Analytics teams can test where Genie reduces ad hoc report queues while keeping governed metrics and permissions in place.
• Genie and AI/BI experiences: Analytics teams can test where Genie reduces ad hoc report queues while keeping governed metrics and permissions in place.
• Databricks Runtime 19: June 15, 2026: Assess whether it affects platform governance, data engineering productivity, AI/BI adoption, sharing controls or workspace operations.
• Databricks Runtime 19 and Databricks Runtime 19 for Machine Learning are in Beta: Assess whether it affects platform governance, data engineering productivity, AI/BI adoption, sharing controls or workspace operations.

Editorial read

Databricks is continuing to package the lakehouse as an operating layer for data products: more AI/BI entry points for business users, more managed pipeline tooling for engineers, and more governance around sharing, audit data and external access. The practical theme is adoption with controls: turn on the useful productivity features, but pair them with app governance, metric ownership and security review.