AI Functions
Recent items mentioning AI Functions across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Databricks' Lakeflow Designer now enables no-code data transformations, including sentiment analysis, through a drag-and-drop UI or AI-powered Genie Code 2. This visual tool also supports AI Prep Search for document chunking and vector database preparation, alongside SQL vector functions for embedding mathematics 3. AI agents and Genie are also being leveraged for transforming maintenance reports 1.
Generated daily from the 3 most recent items mentioning AI Functions. Click any [N] to jump to the source.
Ingest semi-structured data faster and more efficiently with Variant - Now Generally Available
Databricks Variant is now Generally Available, enabling teams to achieve up to 30x faster reads on semi-structured data while handling unpredictable schema changes without pipeline updates. The feature is broadly integrated across the platform, supporting data workloads like Auto Loader and Spark Declarative Pipelines alongside AI tools like Agent Bricks and AI Functions.
Transforming solar and wind maintenance reports with Genie and AI agents
Plenitude now converts unstructured solar and wind maintenance PDFs into a unified, queryable data model using Databricks Genie and AI agents. This enables natural-language querying and visualizations across plants, accelerating multi-plant analysis and laying the groundwork for predictive maintenance.
TutorialsThe New Databricks Lakeflow Designer Is a Game Changer!
Databricks Lakeflow Designer is a visual data preparation tool that allows users to create, add, and transform data using a no-code drag-and-drop UI or AI-powered Genie Code. The video demonstrates how to import data from various sources, profile data, perform complex transformations like data type conversions and sentiment analysis, and then deploy the resulting production-ready PySpark code for scheduling or integration into existing pipelines.
NewsMay 2026 Databricks Updates: No Code ETL, New GPUs and Death of the Dashboard
Databricks announced several updates including AI Prep Search for document chunking and vector database preparation, SQL vector functions for embedding mathematics, and the general availability of multi-table transactions. They also introduced Lakeflow Designer for visual, no-code data pipeline creation and updated their serverless GPU offerings to include H100s.
ReleasesIntroducing Databricks Document Intelligence
Databricks Document Intelligence is a new solution for extracting, processing, and analyzing unstructured data from documents using large language models. It offers a unified platform for document processing, including data extraction, summarization, and question answering, with a focus on accuracy and scalability.
Unity Catalog AI 0.4.0
DatabricksFunctionClient now accepts an optional warehouse_id parameter to execute functions via the Statement Execution API when serverless compute is unavailable, while serverless remains the default execution path. The release requires Python 3.10 or later and fixes issues with Gemini toolkit, LangGraph integration, OSS client function creation, and dependency declarations.
UnityCatalog 0.4.0
Unity Catalog 0.4.0 introduces AWS storage credentials and external locations enabling secure S3 access with automatic temporary credential vending, and makes CREATE TABLE AS SELECT atomic for Delta tables to prevent data corruption. Credential renewal defaults to enabled for long-running Spark jobs, and new DSPy integration allows UC AI functions to serve as tools in LLM agent workflows.
