TikTok Ads Connector (Beta)
Lakeflow Connect unterstützt nun einen verwalteten Connector zur Datenaufnahme aus TikTok Ads.
Lakeflow Connect now supports a managed connector for ingestion from TikTok Ads. See TikTok Ads connector.
Databricks Release Notes
- Einträge
- 1.189
- Quellen
- 1
- Zuletzt aktualisiert
Folge Databricks, um die Release Notes in deinen Feed zu holen.
Discover-Seite und Domains (Beta)
Die Discover-Seite zum Suchen, Durchsuchen und Ansehen von durch Unity Catalog verwalteten Datenobjekten bietet in der Beta Domains, individuelle Kuratierung, KI-basierte Empfehlungen und einen einheitlichen Zugriff auf Tabellen, Dashboards und Genie Agents.
The Discover page provides a centralized interface for searching, browsing, and previewing data assets governed by Unity Catalog. This Beta release introduces:
- Domains: Business-aligned organization layer that groups data assets by functional area (for example, Marketing, or Finance) to improve discovery and stewardship
- Custom curation: Curators can customize and highlight specific assets in their organization or domain
- AI-powered recommendations: Surface popular and valuable datasets to enable curation at scale
- Unified discovery: Access tables, dashboards, Genie Agents, and more in one place
Domains are built on governed tags, allowing you to organize data assets in a way that's best suited for your business needs.
Personal Access Tokens mit Scopes (Beta)
Die Berechtigungen von Personal Access Tokens lassen sich nun über Token-Typ und API-Scopes auf bestimmte API-Operationen beschränken.
You can now limit the permissions of personal access tokens by selecting a token type and adding API scopes. This restricts each token to only the API operations you specify. See Authenticate with Databricks personal access tokens (legacy). This feature is in Beta.
Qwen3-Embedding-0.6B in Public Preview verfügbar
Mosaic AI Model Serving unterstützt Qwen3-Embedding-0.6B als Databricks-gehostetes Modell in Public Preview, nutzbar über Foundation Model APIs pay-per-token.
Mosaic AI Model Serving now supports Qwen3-Embedding-0.6B in Public Preview as a Databricks-hosted model. You can access this model using Foundation Model APIs pay-per-token.
Anthropic Claude Sonnet 4.6 als Databricks-gehostetes Modell
Mosaic AI Model Serving unterstützt Anthropic Claude Sonnet 4.6 als Databricks-gehostetes Modell, nutzbar über Foundation Model APIs pay-per-token.
Mosaic AI Model Serving now supports Anthropic Claude Sonnet 4.6 as a Databricks-hosted model. You can access this model using Foundation Model APIs pay-per-token.
Databricks Runtime 18.1 und 18.1 ML in Beta
Databricks Runtime 18.1 und Databricks Runtime 18.1 ML sind nun in der Beta verfügbar und basieren auf Apache Spark 4.1.0.
Databricks Runtime 18.1 and Databricks Runtime 18.1 ML are now in Beta, powered by Apache Spark 4.1.0.
See Databricks Runtime 18.1 (EoS) and Databricks Runtime 18.1 for Machine Learning (EoS).
AI Gateway (Beta)
AI Gateway (Beta) dient als zentrale Kontrollebene zur Steuerung von LLM-Endpunkten und Coding-Agents und bietet eine umfangreiche Oberfläche, bessere Observability und erweiterte API-Abdeckung.
AI Gateway (Beta) is the enterprise control plane for governing LLM endpoints and coding agents with enhanced features, including a rich UI, improved observability, and expanded API coverage.
See AI governance with Unity Gateway.
File events standardmäßig bei neuen External Locations aktiviert
Beim Anlegen neuer External Locations sind File events nun standardmäßig aktiviert, in der UI werden dabei die nötigen Berechtigungen geprüft, und die Option lässt sich nachträglich deaktivieren.
File events are now enabled by default when you create new external locations. The Databricks file events service sets up cloud resources to detect file changes, enabling more efficient, event-driven storage jobs and ingestion pipelines.
When you create an external location using the UI, Databricks checks for the required permissions and guides you to fix any issues or proceed with a force create. This check does not run in the API, but ingestion pipelines automatically fall back to prevent breakage.
You can disable file events for an external location after creation if needed. For more information, see Set up file events for an external location.
HubSpot Connector (Beta)
Der verwaltete HubSpot-Connector in Lakeflow Connect ermöglicht die Datenaufnahme aus HubSpot Marketing Hub in Databricks.
The managed HubSpot connector in Lakeflow Connect allows you to ingest data from HubSpot Marketing Hub into Databricks. See HubSpot connector.
Databricks SQL 2025.40 in Preview
Databricks SQL Version 2025.40 ist im Preview-Kanal verfügbar und bringt unter anderem allgemein verfügbares SQL scripting, Parameter markers in mehr SQL-Kontexten und eine erweiterte IDENTIFIER-Klausel.
Databricks SQL version 2025.40 is now available in the Preview channel. Review the following section to learn about new features, behavioral changes, and bug fixes.
SQL scripting is generally available
SQL scripting is now generally available. Write procedural logic with SQL, including conditional statements, loops, local variables, and exception handling.
Parameter markers now supported in more SQL contexts
You can now use named (:param) and unnamed (?) parameter markers anywhere a literal value of the appropriate type is allowed. This includes DDL statements such as CREATE VIEW v AS SELECT ? AS c1, column types such as DECIMAL(:p, :s), and comments such as COMMENT ON t IS :comment. This allows you to parameterize a large variety of SQL statements without exposing your code to SQL injection attacks. See Parameter markers.
IDENTIFIER clause expanded to more SQL contexts
The IDENTIFIER clause, which casts strings to SQL object names, is now supported in nearly every context where an identifier is permitted. Combined with expanded parameter marker and literal string coalescing support, you can parameterize anything from column aliases (AS IDENTIFIER(:name)) to column definitions (IDENTIFIER(:pk) BIGINT NOT NULL). See IDENTIFIER clause. …
Standard-Python-Paket-Repositories für Spark Declarative Pipelines (Public Preview)
Workspace-Admins können private oder authentifizierte Paket-Repositories als Standard-pip-Konfiguration für Spark Declarative Pipelines on Lakeflow festlegen, sodass Nutzer ohne explizite index-url oder extra-index-url Pakete installieren können.
Workspace admins can now configure private or authenticated package repositories within workspaces as the default pip configuration for Spark Declarative Pipelines on Lakeflow. This allows users in the workspace to install packages from internal Python repositories without explicitly defining index-url or extra-index-url values.
See Configure default package repositories for more details.
Supervisor Agent ist allgemein verfügbar
Supervisor Agent zur Orchestrierung von KI-Agents und Tools bei komplexen Aufgaben ist in ausgewählten US-Regionen für Workspaces ohne Enhanced Security and Compliance allgemein verfügbar.
Use Supervisor Agent to create a multi-agent supervisor system that orchestrates AI agents and tools to work together on complex tasks.
Supervisor Agent is now Generally Available in select US regions for workspaces without Enhanced Security and Compliance features.
Neue Dokumentation für Agents auf Databricks Apps
Neue Dokumentation und Projektvorlagen erklären, wie sich KI-Agents mit Bibliotheken wie LangGraph, PyFunc und OpenAI Agent SDK erstellen und auf Databricks Apps bereitstellen lassen.
New documentation and project templates are available for building and deploying AI agents on Databricks Apps. Author agents with popular libraries like LangGraph, PyFunc, and OpenAI Agent SDK, then deploy them on Databricks Apps. See Run agents on Databricks Apps using the legacy agent server.
Databricks Apps aus Git-Repositories bereitstellen (Beta)
Databricks Apps lassen sich nun direkt aus Git-Repositories von jedem Branch, Tag oder Commit bereitstellen, ohne Dateien in den Workspace hochzuladen.
You can now deploy Databricks apps directly from Git repositories without uploading files to the workspace. Configure a repository for your app and deploy from any branch, tag, or commit. See Deploy from a Git repository.
Query Tags für SQL Warehouses (Public Preview)
Auf Databricks SQL Warehouses lassen sich SQL-Workloads jetzt mit eigenen Key-Value-Tags versehen, die in system.query.history und auf der Query-History-Seite erscheinen und per Session-Konfiguration, SET QUERY_TAGS oder Konnektoren wie dbt, Power BI, Tableau, Python, Node.js, Go, JDBC und ODBC gesetzt werden können.
You can now apply custom key-value tags to SQL workloads on Databricks SQL warehouses for grouping, filtering, and cost attribution. Query tags appear in the system.query.history table and on the Query History page of the Databricks UI, allowing you to attribute warehouse costs by business context and identify sources of long-running queries. Tags can be set using session configuration parameters, the SET QUERY_TAGS SQL statement, or through connectors including dbt, Power BI, Tableau, Python, Node.js, Go, JDBC, and ODBC. See Query tags.
Dashboard-Erweiterungen und neue Funktionen in Databricks
Dashboards erhalten unter anderem parametrisierte Dashboard-Tasks in Jobs, Themes auf Workspace-Ebene, Refresh-only-Zeitpläne, Tag-Suche, standardmäßig aktiviertes Genie und einen Assistant für die Dashboard-Erstellung (Beta).
- Parameterized dashboard tasks: Dashboard tasks in Jobs can now be parameterized, allowing job parameters to dynamically set dashboard filter values at runtime.
- Workspace-level dashboard themes: Dashboard themes can now be configured at the workspace level. See Manage AI/BI dashboard workspace themes.
- Top-aligned text widgets: Text widgets now default to top-aligned text.
- Refresh-only schedules for individual data permissions: Authors and viewers of dashboards published with individual data permissions can now create refresh-only schedules that trigger cache refreshes without sending notifications, improving dashboard load times. See Subscribe to an existing schedule.
- Genie Agents (formerly Genie Spaces) enabled by default: Genie is now on by default for published dashboards, allowing viewers to ask questions using the Ask Genie button.
- Tag search: Tag search is now supported for dashboards, allowing users to find dashboards by their assigned tags.
- Assistant for dashboard authoring (Beta): Authors can now automate multi-step dashboard workflows using natural language prompts to create datasets, visualizations, layouts, and filters. The legacy inline-visualization prompting experience now redirects to the Assistant side pane. See Use Genie Code for dashboard authoring.
Thumbnail-Upload für Databricks Apps in Genie One
Kunden können jetzt Thumbnails als Vorschaubild für Databricks Apps hochladen.
- App thumbnail uploads: Customers can now upload thumbnails as the preview image for Databricks apps.
Verbesserungen für Genie Agents (ehemals Genie Spaces)
Bei Genie Agents werden Compute-Credentials nun für alle Nutzer eingebettet, Spaltenlisten im Configure-Tab sind paginiert, und die Tag-Suche wird unterstützt.
- Embedded compute credentials: Compute credentials are now embedded for all users of a Genie Agent (formerly Genie Space). These credentials are embedded when a Genie Agent (formerly Genie Space) author changes the attached warehouse in the Genie Agent (formerly Genie Space).
- Paginated column lists: Column lists in the Configure tab are now paginated for improved usability, especially for rendering tables with many columns.
- Tag search: Tag search is now supported for Genie Agents (formerly Genie Spaces), allowing users to find spaces by their assigned tags.
Umbenennung: Multi-Agent Supervisor in Supervisor Agent
Agent Bricks' Multi-Agent Supervisor wurde in Supervisor Agent umbenannt.
Agent Bricks's Multi-Agent Supervisor has been renamed to Supervisor Agent. See Use Supervisor Agent to create a coordinated multi-agent system.
Google Ads Connector (Beta)
Der verwaltete Google Ads Connector in Lakeflow Connect ermöglicht es, Daten aus Google Ads in Databricks zu laden.
The managed Google Ads connector in Lakeflow Connect allows you to ingest data from Google Ads into Databricks. See Google Ads connector.