August 2026

Azure Databricks released these features and improvements in August 2026.

Note

Releases are staged. Your Azure Databricks account might not be updated until a week or more after the initial release date.

SQL alert task in Lakeflow Jobs is now available by default for workspaces with the compliance security profile enabled

August 13, 2026

The SQL alert task in Lakeflow Jobs now supports all compliance security profile standards that Azure Databricks supports and is available by default for workspaces with the compliance security profile enabled.

Use the task to evaluate a Databricks SQL alert as part of a Lakeflow Job. The task returns its evaluation state as a task output value, so downstream tasks can branch on the result.

See SQL alert task for jobs.

SendGrid connector (Beta)

August 13, 2026

The SendGrid connector is now available in Lakeflow Connect in Beta. Workspace admins can control access to this feature from the Previews page.

The connector ingests the SendGrid subuser directory and IP access-management activity for the parent account and each subuser into Azure Databricks. Authentication uses a SendGrid parent-account API key, with support for both the global and EU data-residency endpoints.

See SendGrid connector.

Share data behind a firewall with SecureConnect is generally available

August 13, 2026

OpenSharing SecureConnect is now generally available. Providers can share data from cloud storage behind a firewall or private endpoint without adding each recipient's network to an allowlist, and recipients access the shares using their existing OpenSharing setup. See Share data behind a firewall with SecureConnect and OpenSharing recipient firewall configuration for SecureConnect.

Azure Databricks is expanding SecureConnect networking billing to more charge types and access scenarios, including charges for recipients. See Expanded networking billing for OpenSharing SecureConnect.

Gmail connector in Lakeflow Connect (Beta)

August 13, 2026

You can now ingest data from a Gmail mailbox into Azure Databricks using the managed Gmail connector in Lakeflow Connect. The connector ingests messages, labels, drafts, filters, and profile data using a Google service account, and syncs the messages and message_labels tables incrementally through the Gmail History API. This feature is in Beta.

See Gmail connector.

Google Drive Audit Logs connector is now available in Beta

August 13, 2026

The managed Google Drive Audit Logs connector in Lakeflow Connect is now available in Beta. You can use the connector to ingest Google Drive audit activity events such as file views, edits, downloads, and sharing changes from Google Workspace into Azure Databricks. See Google Drive Audit Logs connector.

Google Gemini 3.7 Flash now available as a Databricks-hosted model

August 13, 2026

Model Serving now supports Google Gemini 3.7 Flash as a Databricks-hosted model. You can access this model using Foundation Model APIs.

Customers are responsible for ensuring their compliance with applicable terms.

Define business concepts with Pages (Beta)

August 12, 2026

You can now use Pages to give business terms, acronyms, and KPIs a single governed definition. Part of Unity Catalog semantics, Pages form the human-modeled layer of the Genie Ontology that Genie One draws on to ground its answers. See Pages.

Custom trace views in the MLflow trace explorer are in Beta

August 12, 2026

You can now build custom trace views in the Azure Databricks MLflow trace explorer. Describe the layout you want in plain language, and Genie generates a reusable view that surfaces the trace fields, metrics, and feedback controls most relevant to your review workflow. See Build a custom trace view.

Google Search Console connector is available in Beta

August 12, 2026

The managed Google Search Console connector is now available in Beta in Databricks Lakeflow Connect. Use it to ingest search performance data, site metadata, and sitemap information from Google Search Console into Azure Databricks.

The connector supports daily and hourly search analytics dimensions (date, country, device, page, query), as well as verified sites and submitted sitemaps. Incremental ingestion is supported for all search analytics tables.

Unity Catalog ABAC GRANT policies support more securable types in Beta

August 11, 2026

ABAC GRANT policies dynamically grant Unity Catalog privileges to securables whose governed tags match a condition. These policies were previously only available for models, but now also support model services, model provider services, Model Context Protocol (MCP) services, agent services, and skills. All supported types are in Beta.

For more information, see ABAC GRANT policies (Beta).

READ METADATA privilege is GA

August 10, 2026

The READ METADATA privilege is now generally available in Unity Catalog.

READ METADATA provides read-only visibility into the same metadata available to the object's owner or a user with MANAGE, including security-sensitive information that BROWSE doesn't expose, such as privilege grants, row filters and column masks, and ABAC policies. It's intended for users who inspect and debug access controls, such as security auditors, data governance teams, and SREs.

You can grant READ METADATA on a range of object types. For the full list of applicable object types, see READ METADATA.

See READ METADATA.

Notion connector (Beta)

August 10, 2026

The managed Notion connector in Lakeflow Connect is now available in Beta. The connector ingests pages, data sources, databases, blocks, comments, and users from Notion into Azure Databricks. See Notion connector.

Orchestrate continuous pipelines with continuous jobs

August 10, 2026

You can now run a continuous Lakeflow pipeline with a continuous job. When a continuous job orchestrates a pipeline, the job determines the execution mode, and the pipeline can use job-level features such as serverless Standard performance mode and additional notification destinations. Databricks recommends this pattern over the pipeline's built-in continuous setting, which is not being removed.

See Run a continuous pipeline with a continuous job and Run a pipeline continuously with a continuous job.

Target an Azure capacity reservation group for classic compute

August 10, 2026

You can now target an Azure capacity reservation group when you create or edit a classic cluster or instance pool, using the new capacity_reservation_group field in azure_attributes. Capacity reservations guarantee compute capacity for constrained VM types, including GPUs, that might otherwise fail to launch because of Azure stockouts. The field is available through the Clusters and Instance Pools APIs and the Azure Databricks SDKs on workspaces that use VNet injection.

Alert system tables are in Public Preview

August 10, 2026

The system.alert schema is in Public Preview. This schema contains the alerts table, which records the configuration of every alert, and the alert_evaluation_history table, which records one row per alert evaluation. Use these tables to audit alert definitions, analyze evaluation trends, and monitor alert workloads at scale.

See Alert system tables reference.

Tag automations (Beta)

August 7, 2026

Tag automations are now in Beta. A tag automation assigns or removes governed tags on the Unity Catalog tables or volumes that match conditions you define, so tags stay accurate as your data and metadata change. Use them to certify trusted data, deprecate stale data, roll up column sensitivity to a table-level tier, flag assets that are missing required tags, and clean up outdated tags.

You can build an automation by describing your rule to Genie in natural language, or by filling in the scope, conditions, and action yourself. Saving an automation starts a dry run, which records the assets it would match without assigning any tags.

See Automate tag assignment.

Change data feed on materialized views (Beta)

August 7, 2026

You can now read a change data feed from a materialized view created in a Lakeflow pipeline or in Databricks SQL, using Databricks Runtime 18 LTS or above. Use this to replicate materialized view changes to destinations outside Azure Databricks, or to keep a history of materialized view changes for auditing and reporting. Materialized views use automatic change data feed, so the change data feed is available for any materialized view with row tracking enabled, without configuring each materialized view. This feature is in Beta.

See Read a change data feed from a materialized view.

ai_search function (Beta)

August 7, 2026

The ai_search() SQL function retrieves information from one or more AI Search indexes configured as knowledge sources. Given a natural-language query, the function generates optimized search queries, retrieves and deduplicates results across sources, reranks them by relevance, and returns the most relevant documents. By default, it also synthesizes a grounded natural-language answer over those documents.

Use ai_search to enrich operational data with relevant context at scale, build batch retrieval-augmented generation (RAG) pipelines, or expose retrieval as a tool to a compound AI system. This feature is in Beta.

See ai_search function.

JAR tasks on serverless compute are now generally available

August 6, 2026

Package Scala or Java code as a JAR and run it as a task in a Lakeflow Job on serverless compute, without provisioning or managing clusters. See Create and run JARs on serverless compute.

Databricks-managed MCP connectors are now integrated with Unity AI Gateway (Beta)

August 6, 2026

All Databricks-managed MCP connectors for Genie One and Genie Code have migrated to the Unity AI Gateway. This brings Databricks-managed connectors under Unity AI Gateway, enabling centralized governance, access controls, and visibility alongside your other MCP servers and tools.

Users must reauthenticate their connectors to continue using them with Genie One and Genie Code. A notification in Genie prompts impacted users to reconnect.

REPLACE USING flows for pipelines (Beta)

August 5, 2026

REPLACE USING flows are now available in Lakeflow pipelines in Beta.

Use a REPLACE USING flow when your source is a series of partial snapshots keyed by column. On each update, the flow replaces all rows that match the specified key columns and leaves all other rows unchanged. A SEQUENCE BY column orders the updates, so the highest sequence for a key always wins, even when updates arrive out of order. REPLACE USING doesn't require a change data capture feed with explicit insert, update, and delete operations, and it doesn't require a primary key.

See Partial snapshot replacement with REPLACE USING flows.

Window measures on a numeric index column in metric views

August 5, 2026

Window measures in metric views support a unitless numeric offset and numeric trailing and leading ranges on a consecutive integer order column. Use a numeric index to compare along a business period that is not a calendar date, such as a fiscal week or a 4-4-5 accounting period. See Window measures on a numeric index column.

Search and replace text across files

August 5, 2026

You can now search and replace text across files in a Azure Databricks Git folder or Declarative Automation Bundles project from the Search pane. See Search and replace across files.

Lakebase support for compliance security profile standards

August 5, 2026

Lakebase is now available by default for workspaces with the compliance security profile enabled and either HIPAA, C5, or TISAX controls selected, and for workspaces with the compliance security profile enabled without a compliance standard. See Data protection.

Genie One is now available in Google Sheets

August 5, 2026

You can now use Genie One directly in the Databricks Connector for Google Sheets. Query your governed Azure Databricks data in natural language and import the results into Google Sheets as native rows and columns. See Use Genie in Google Sheets.

Genie One is now available in Microsoft Excel

August 5, 2026

You can now use Genie One directly in the Azure Databricks Excel Add-in. Query your governed Azure Databricks data in natural language and import the results into Microsoft Excel as native rows and columns. See Use Genie in Microsoft Excel.

Unity AI Gateway is now generally available

August 4, 2026

Unity AI Gateway is now generally available. Unity AI Gateway is the Azure Databricks governance solution for enterprise AI, part of Unity Catalog. With Unity AI Gateway you can:

  • Control which AI services teams can use.
  • Route and manage AI traffic across providers.
  • Govern MCP servers to control access and costs.
  • Monitor usage, cost, access, and lineage from one place.

Some capabilities, including service policies and agent services, remain in Beta.

See AI governance with Unity AI Gateway and AI governance guide.

Sharing Delta tables with Iceberg reads enabled is now generally available

August 4, 2026

Sharing Delta tables with Iceberg reads enabled is now generally available. See Delta Lake feature support matrix.

Sharing Delta tables with deletion vectors and column mapping is now generally available

August 4, 2026

Sharing Delta tables with deletion vectors or column mapping enabled is now generally available. See Add tables with deletion vectors or column mapping to a share.

SharePoint connector (GA)

August 4, 2026

The SharePoint connector in Lakeflow Connect is now Generally Available. See SharePoint connector.

Format AI/BI dashboard widget titles and descriptions with rich text

August 4, 2026

Visualization widget titles and descriptions support rich text formatting. Click a widget's title or description to open a formatting toolbar, where you can apply text styling, links, and tables, or type markdown directly.

See Format widget titles and descriptions.

PagerDuty connector (Beta)

August 4, 2026

The managed PagerDuty connector in Lakeflow Connect is now available in Beta. The connector ingests incident, on-call, service, and audit data from PagerDuty into Azure Databricks. See PagerDuty connector.

NetSuite connector (GA)

August 4, 2026

The managed NetSuite connector in Lakeflow Connect is now generally available. The connector allows you to ingest ERP and financial data from Oracle NetSuite into Azure Databricks. See NetSuite connector.

Google Drive connector (GA)

August 4, 2026

The Google Drive connector in Lakeflow Connect is now Generally Available. See Google Drive connector.

Full page Genie Code is now generally available

August 4, 2026

Full page Genie Code is now generally available. Full page Genie Code is a command center experience where the active chat is shown prominently, notebooks and files open as tabs alongside it, and you can run multiple chats in parallel. Start work directly from Genie Code, and personalize how it works with skills, instructions, and MCP servers. See Full page Genie Code.

Databricks Runtime maintenance updates (08/04)

August 4, 2026

New maintenance updates are available for supported Databricks Runtime versions. These updates include bug fixes, security patches, and performance improvements. For details, see:

Secrets in Unity Catalog is GA

August 3, 2026

Secrets in Unity Catalog is now generally available. You can store, govern, and access secrets as securable objects in Unity Catalog. A Unity Catalog secret uses the three-level namespace (catalog.schema.secret) and is governed by Unity Catalog privileges, so you can apply the same access controls and auditing that you use for other data assets. See Secrets in Unity Catalog.

Organize your work with spaces

August 3, 2026

You can now use spaces to organize your work in the Azure Databricks editor. Each space lets you focus on a specific folder or project while preserving your open tabs, so you can easily pick up where you left off.

See Organize your work with spaces.

Serverless compute access control is now generally available

August 3, 2026

Serverless compute access control is now generally available. Workspace admins can govern who runs serverless workloads through two built-in serverless compute objects: Default Interactive Compute (governs access to notebooks and Databricks Connect) and Default Automated Compute (governs access to jobs and Spark Declarative Pipelines on Lakeflow). All workspace users have Can Use on both objects by default, so existing workloads continue to run without changes.

See Serverless compute access control.

Automatic cost attribution for materialized views and streaming tables in Databricks SQL

August 3, 2026

Materialized view and streaming table refreshes in Databricks SQL now inherit custom tags from the enclosing SQL warehouse environment. These tags propagate to billing surfaces like system.billing.usage, enabling proper cost attribution of materialized view and streaming table updates back to the source SQL warehouse. See Attribute costs to the SQL warehouse with custom tags.

MANAGE no longer requires USE CATALOG or USE SCHEMA on the same object

August 3, 2026

The MANAGE privilege no longer requires a usage privilege (USE CATALOG or USE SCHEMA) on the object where MANAGE is granted. This change makes MANAGE behave more consistently with object ownership.

Previously, MANAGE on a catalog or schema took effect only if you also held the appropriate usage privileges on that object. You still need usage privileges on any parent containers:

  • MANAGE on a catalog requires no usage privileges.
  • MANAGE on a schema requires USE CATALOG on the parent catalog, but no longer requires USE SCHEMA on the schema.
  • MANAGE on a table, view, volume, or function is unchanged. It still requires USE CATALOG on the parent catalog and USE SCHEMA on the parent schema.

All other privileges are unaffected and still require usage privileges as prerequisites, even for users with MANAGE. For example, querying a table still requires SELECT on the table, plus USE CATALOG and USE SCHEMA on the parent catalog and schema.

If you granted MANAGE broadly without also granting the corresponding usage privileges, those grants are now active, and holders can manage the object. Azure Databricks recommends auditing existing MANAGE grants to confirm they reflect your intended access.

See Usage privilege requirements for MANAGE.

Genie One and Genie Agents free usage extended through January 31, 2027

August 3, 2026

Free usage of Genie One and Genie Agents is extended through January 31, 2027. This promotion was previously set to end on July 31, 2026. Budget controls do not apply to these products during the promotional period.

The promotion covers usage by users only. Service principals are excluded, and their Genie One and Genie Agents usage continues to be billed.

Genie Code is not included in the free usage promotion. Each Genie Code user continues to receive 150 free Databricks units (DBUs) per month, and usage above that allowance receives a 25% promotional discount through January 31, 2027. The promotional discount is combined with applicable discounts on the Serverless Real-Time Inference SKU. For more information about pricing, see Azure Databricks pricing.

To track free and billed usage in the billing system tables, see Monitor and understand your Genie cost.

Filter updates for the Azure Databricks Excel Add-in

August 3, 2026

The Azure Databricks Excel Add-in now supports cascading filters and case-insensitive string matching. Additionally, the LIKE, NOT LIKE, IN, NOT IN, and ILIKE filters are no longer available in the filters list.

See Filter imported data.

Refresh materialized views and streaming tables without exempting the pipeline owner from ABAC policies

August 3, 2026

You can now refresh a pipeline-managed materialized view or streaming table even when the pipeline owner or run-as identity is subject to an attribute-based access control (ABAC) policy.

Previously, refreshing a materialized view or streaming table managed by a pipeline required the pipeline owner or run-as identity to be exempt from any applicable ABAC policy. That limitation has been removed.

When a pipeline refreshes a materialized view or streaming table, policies are evaluated using the pipeline owner's or run-as identity. If you don't want the persisted data to be masked or filtered, add the pipeline owner or run-as identity to the EXCEPT clause of ABAC policies associated with all the tables being read during the pipeline refresh. Use the TO clause to specify which users and groups receive masked or filtered data.

See ABAC policies on materialized views and streaming tables.

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