--- title: "Unity Catalog" description: "112 posts about Unity Catalog, summarised, each linking to the original." --- # Unity Catalog > 112 posts about Unity Catalog, summarised, each linking to the original. ## Articles ### [What’s new with Unity Catalog at Data + AI Summit 2026](https://yomu.fyi/post/what-s-new-with-unity-catalog-at-data-ai-summit-2026.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: The Unity Catalog Product and Engineering Team - Published: Jun 16, 2026 At Data + AI Summit 2026, Unity Catalog announcements position the catalog as a runtime governance layer for enterprise data and AI, organized around control, context, and choice. Control additions include Unity AI Gateway for governing models, agents, MCP services, skills, and tools; contextual service policies can allow, deny, or require approval for runtime actions, while budgets, hard caps, tracing, and guardrails address spend, investigation, and safety. Context additions include Glossary and Domains for business meaning and scoped asset organization, plus Metrics that standardize KPIs for SQL, BI tools, APIs, and agents; Genie Ontology is described as a continuously learned enterprise context layer. Choice additions span cross-cloud and cross-region addressability, managed disaster recovery, Delta and Iceberg interoperability, multimodal and geospatial types, and open sharing of data, AI assets, and applications across organizations. ### [Agent Bricks: Data + AI Summit 2026](https://yomu.fyi/post/agent-bricks-data-ai-summit-2026.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Hanlin Tang, Kasey Uhlenhuth, Akhil Gupta, Patrick Wendell - Published: Jun 16, 2026 At Data + AI Summit 2026, Databricks announced Agent Bricks as a comprehensive developer platform for building and operating agents, extending a product launched the previous year. The announcement frames the core agent loop as only 1% of the work, with token capacity, deployment, security, evaluation, monitoring, context, and sharing forming the remaining infrastructure burden. Agent Bricks addresses choice, context, and control through support for multiple proprietary, open-source, and custom models, any agent harness, MCP-connected data, Genie Ontology, managed memory, document intelligence, sandboxes, and governed tools. Unity AI Gateway adds catalogs, fine-grained access controls, budgets, traffic routing, contextual policies, monitoring, and registry support for agents, tools, and models. Databricks says more than 100,000 agents have been built and customers including AstraZeneca, 7-Eleven, Fox Corporation, and Block have shipped agents on the platform. ### [AI governance at Data + AI Summit 2026: What’s new with Unity AI Gateway](https://yomu.fyi/post/ai-governance-at-data-ai-summit-2026-what-s-new-with-unity-ai-gateway.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: David Nasi, Stefania Leone, Ahmed Bilal, Kevin Stumpf, Martin Grund, Vladimir Kolovski, Kelly Albano - Published: Jun 16, 2026 Databricks announces new Unity AI Gateway capabilities for governing enterprise AI as organizations operate multi-model, multi-agent, and multi-vendor estates connected to models, MCP services, APIs, and tools. The update adds unified spend visibility, granular attribution, hard spend caps, and smart routing, alongside Unity Catalog support for registering and governing models, MCP services, agents, and skills. Contextual Service Policies, in Beta, can allow, deny, or require approval for actions based on users, agents, models, tools, services, or request and response contents, with guardrails for risks such as PII exposure and prompt injection. The announcement also covers end-to-end tracing, coding-agent analysis with Genie, incident investigation with Lakewatch, ecosystem integrations, and Managed Omnigent on Databricks in Beta. ### [Lakeflow: A new era of agentic data engineering](https://yomu.fyi/post/lakeflow-a-new-era-of-agentic-data-engineering.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Bilal Aslam, Ray Zhu, Manish Dalwadi, Saad Ansari, Giselle Goicochea - Published: Jun 16, 2026 Databricks announces a major evolution of Lakeflow, its unified platform for data engineering across ingestion, transformation, and orchestration, with capabilities centrally governed by Unity Catalog. Genie Code and generally available Lakeflow Designer support agentic and no-code pipeline development, while Genie ZeroOps monitors production assets, analyzes failures, proposes fixes, and validates them in a governed sandbox before human approval. Lakeflow Connect expands to more than 100 managed connectors, and Zerobus Ingest adds Kafka-compatible, gRPC, REST, SDK, and OpenTelemetry interfaces for high-volume event ingestion. Real-Time Mode for Spark Declarative Pipelines reaches Public Preview with end-to-end latency as low as 5 milliseconds, alongside declarative APIs and expanded Lakeflow Jobs integrations. The release also adds data-readiness triggers and external orchestration for systems including Snowflake, REST APIs, Slack, and PagerDuty. ### [Introducing Genie ZeroOps: Put your data and AI operations on autopilot](https://yomu.fyi/post/introducing-genie-zeroops-put-your-data-and-ai-operations-on-autopilot.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Bilal Aslam, Lennart Kats, Ray Zhu, Mike Del Balso, Ori Zohar - Published: Jun 16, 2026 Genie ZeroOps is an autonomous background agent for monitoring and operating data and AI assets, including jobs, pipelines, tables, and ML models. It continuously detects visible and silent failures, uses Unity Catalog lineage and platform observability to assess root causes, generates remediation through development workflows, and verifies fixes in isolated sandboxes. These environments use shallow, zero-copy table clones, scoped permissions, and network isolation, so proposed changes run against real data without touching production or applying anything before approval. For ML workloads, the agent can diagnose degraded predictions, train a candidate on corrected features, evaluate it against the production model’s existing eval suite and criteria, and support live-traffic ramping when it is measurably better. Genie ZeroOps is entering private preview in the coming weeks, initially supporting jobs, pipelines, tables, and ML workloads; Apps and Lakebase databases are on the roadmap. ### [Unifying Data and Governance in the Agentic Era: What’s New with Azure Databricks](https://yomu.fyi/post/unifying-data-and-governance-in-the-agentic-era-what-s-new-with-azure.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Isaac Gritz, Toussaint Webb, Ben Tripp, Kiriana Stukas - Published: Jun 16, 2026 At Data + AI Summit 2026, Azure Databricks announced capabilities aimed at moving enterprises from experimental AI pilots to production-grade automated workflows by unifying data, productivity tools, marketing, and governance on Azure. Its Agentic Data foundation introduces LTAP, combining analytical data, streaming pipelines, and live application transactions in one lakehouse storage copy; Lakebase adds a managed serverless Postgres engine with copy-on-write branching, while Lakehouse//RT targets millisecond responses for high-concurrency workloads. Genie integrations for Microsoft Teams, M365 Copilot, Excel, and SharePoint bring governed lakehouse intelligence and ingestion into daily work, alongside tools for agents, applications, pipelines, and autonomous operations. CustomerLake adds Profile Agents and Campaign Agents for customer profiles and personalization, while Genie Ontology and Unity AI Gateway provide semantic context, rate limits, content filtering, and spend controls. ### [Introducing Genie One, Genie Agents, and Genie Ontology](https://yomu.fyi/post/introducing-genie-one-genie-agents-and-genie-ontology.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Sydney Sundell, Ken Wong, Elise Georis - Published: Jun 16, 2026 Databricks announces Genie One, Genie Agents, and Genie Ontology to help enterprises answer business questions and act on data whose context is scattered across dashboards, queries, documents, tickets, and chats. Genie One connects data and business tools through Lakehouse federation, Lakeflow Connect, native integrations, Slack, Teams, mobile apps, schedules, alerts, document creation, custom skills, and MCP support. Genie Agents evolve Genie Spaces into domain-specific agents that can reason over structured and unstructured data, execute multi-step workflows, and be created from a prompt. Genie Ontology builds a permission-aware living graph from enterprise assets, weighting sources by authority, usage, certification, and freshness. In an internal 28-question benchmark, Genie answered 84.5% correctly on the first attempt and delivered twice the speed of the strongest coding agent. ### [Introducing CustomerLake: The Agentic CDP embedded in Databricks](https://yomu.fyi/post/introducing-customerlake-the-agentic-cdp-embedded-in-databricks.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Tasso Argyros, Justin DeBrabant, Michael Trapani, Dan Morris, Katy Yuan - Published: Jun 16, 2026 Databricks announces CustomerLake, an Agentic Customer Data Platform embedded natively in its lakehouse, bringing Customer 360, identity resolution, audience building, campaign automation, activation, and personalization alongside governed data and AI models. The announcement addresses fragmented identities, stale audiences, manual campaign workflows, and the duplication and governance burden created by separate martech systems. CustomerLake uses Unity Catalog and Lakehouse Federation to access customer data across Databricks, Snowflake, Google BigQuery, cloud object storage, operational databases, and other enterprise systems, while Profile Agents create business-ready profiles and Campaign Agents build audiences, recommend actions, activate channels, and optimize engagement. Its operating model is described as embedded, democratized, and autonomous, with Agentic Identity Resolution combining deterministic, probabilistic, and agentic workflows. CustomerLake is now available in Private Preview and launches with an open partner ecosystem. ### [What is an open lakehouse? Open data standards, explained.](https://yomu.fyi/post/what-is-an-open-lakehouse-open-data-standards-explained.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Lisa Cao - Published: Jun 16, 2026 The piece defines an open lakehouse as a lakehouse whose storage, table format, processing engine, catalog, and ML and AI tooling use open standards and remain interchangeable. It contrasts this architecture with warehouses, lakes, and proprietary lakehouses, emphasizing low-cost object storage, ACID transactions, governance, schema guarantees, and the ability to change engines without rewriting data. Its reference stack combines open table formats such as Delta Lake and Apache Iceberg with Apache Parquet, Apache Spark, Unity Catalog, and MLflow, while allowing engines including DuckDB, Trino, and PyIceberg to work on the same data. The article also distinguishes open standards from open-source code, explains that a table format is only one layer of the stack, and states that the components can be self-hosted or consumed through a managed service. ### [Announcing New OpenSharing and Marketplace capabilities for the AI era](https://yomu.fyi/post/announcing-new-opensharing-and-marketplace-capabilities-for-the-ai-era.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Akram Chetibi, Harish Gaur, Huey Han, Tia Chang, Mengxi Chen, Lin Zhou, DJ Sharkey - Published: Jun 15, 2026 Databricks announces new OpenSharing and Marketplace capabilities aimed at sharing data, AI assets, and partner applications without moving data. OpenSharing, a Linux Foundation project and evolution of Delta Sharing, adds vendor-neutral sharing for Agent Skills, AI models, unstructured data, Iceberg clients, Lakebase tables and change data feed, plus governed multi-cloud connectivity and agentic sharing through Genie Agent Sharing. The release also introduces identity resolution in Databricks Clean Rooms, where partners such as LiveRamp and Acxiom can work against protected first-party data without seeing raw customer records. Third-party apps are now available through Databricks Marketplace, allowing customers to deploy them in their workspaces without separate infrastructure, while providers gain distribution. Marketplace Commit Drawdown lets customers use existing Databricks universal commits to acquire Marketplace data and AI solutions. ### [What is Document AI?](https://yomu.fyi/post/what-is-document-ai.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Databricks Staff - Published: Jun 15, 2026 Document AI uses machine learning, natural language processing (NLP) and optical character recognition (OCR) to extract, classify and understand information from structured, semi-structured and unstructured documents. Unlike OCR alone, it interprets layout and context, turning files such as invoices, contracts and emails into structured, actionable data through ingestion, OCR, layout parsing, entity extraction, classification, validation and, when needed, human review. Modern systems add large language models for summarization, document Q&A and zero-shot extraction, but hallucination risk makes validation and human oversight essential, particularly in regulated settings. The guide also describes Databricks Document Intelligence, which processes and stores documents alongside organizational data under Unity Catalog, using AI Functions, Variant and Lakeflow Jobs to create governed, queryable workflows without moving data between systems. ### [From Wall Street to Data Platforms](https://yomu.fyi/post/from-wall-street-to-data-platforms.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Andrea Fernández, Kim Hatton - Published: Jun 13, 2026 Kim Hatton, Databricks’ Global Financial Services Marketing Leader, describes how two decades in regulated financial-services marketing led her toward technology and data-centered strategy. She says financial institutions still need to unlock value while navigating compliance, but their divisions and systems make a unified customer view difficult. Her account points to Unity Catalog’s unified governance and single source of truth for breaking down silos and supporting requirements including GDPR, customer identity, and sensitive workloads. It also describes Lakebase’s separation of compute and storage for faster ML/AI agent experimentation, alongside Genie’s plain-language data analysis, which can reduce work that otherwise takes months and expensive third parties. Hatton connects these tools with faster, more confident marketing and accurate decision-making in regulated workflows, while also describing Databricks’ inclusive, high-energy culture. ### [What is enterprise intelligence?](https://yomu.fyi/post/what-is-enterprise-intelligence.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Databricks Staff - Published: Jun 12, 2026 Enterprise intelligence (EI) is presented as an organization-wide capability combining business intelligence, knowledge management, enterprise search and AI to turn structured and unstructured data into decisions and actions. Unlike traditional BI, which centers on dashboards, reports and structured data, EI connects these capabilities through a shared architecture and governed business context. The described stack includes a lakehouse-based data foundation, batch and streaming pipelines, governance, semantics, analytics, search, machine learning, generative AI and a decision layer. Shared definitions such as “active customer” and “monthly revenue” are intended to keep dashboards, queries and AI agents aligned, while maintained context addresses knowledge that becomes stale as the business changes. The result described is a common trusted source from which people, applications and agents can produce insights and initiate actions. ### [Enabling Evolutionary Database Development: Database branching with Lakebase, the conclusion](https://yomu.fyi/post/enabling-evolutionary-database-development-database-branching-with-lak.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Pramod Sadalage, Kevin Hartman - Published: Jun 12, 2026 The post concludes a series on how copy-on-write database branching in Databricks Lakebase changes team-scale evolutionary database development without changing its underlying methodology. For a team of fifty developers, long-running tier branches and ephemeral feature branches form a parent-linked promotion hierarchy, replacing separately provisioned environment instances and enabling promotion by merge, rollback by repoint, and computable schema divergence. Governance is declared once and inherited per branch, with policies intended to prevent transitions that contradict the parent chain; Unity Catalog captures metadata for attribution and audit. The DBA's role becomes platform engineering, while agents operate inside an executable SCM state machine with documented inputs, outputs, schema validation, and enforced gates. An optional TDD layer adds dedicated roles, acceptance-criterion scenarios, RED-GREEN-REFACTOR cycles, and artifact contracts, and the conclusion presents the resulting workflow as operational for human and agent practitioners. ### [Talk to all your data, wherever it lives](https://yomu.fyi/post/talk-to-all-your-data-wherever-it-lives.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: John Spencer - Published: Jun 12, 2026 Lakehouse Federation addresses the challenge of reasoning across enterprise data spread among AWS Glue, Snowflake, Oracle, BigQuery, Postgres, and legacy formats without first migrating it. By connecting external sources in place and syncing their metadata into Unity Catalog, Databricks applies shared permissions, lineage, and access controls while preserving source data. Federated comments and descriptions give Genie schema context, while Unity Catalog Semantics lets teams define governed metrics such as ROI once for consistent use across Genie, dashboards, and notebooks. The example connects an AWS Glue marketing database, carries its metadata, defines an ROI metric view, and asks Genie which campaigns led ROI last quarter. The post reports an immediate, accurate answer from live Glue data, and describes planned richer semantics, broader federation, and possible performance gains from managed tables. ### [Unlocking semantics for AI: How Mercedes-Benz Korea built trusted “Talk to Data” at scale](https://yomu.fyi/post/unlocking-semantics-for-ai-how-mercedes-benz-korea-built-trusted-talk.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Sai Yang, Fares Kamal, Alina Kamal, Andreas Jäck, Johannes Laufer, Manuel Culebras - Published: Jun 11, 2026 Mercedes-Benz Korea piloted a “Talk to Data” architecture that extends its Databricks analytics foundation with a governed semantic layer for enterprise AI, rather than treating the effort as a chatbot project. The design moves Power BI DAX KPI logic into Unity Catalog Business Semantics and Metric Views, keeping sources, joins, measures, dimensions, comments, and synonyms alongside governed Lakehouse data. Genie spaces use curated metric views for domain questions, while Agent Bricks composes persona-based agents, with Unity Catalog enforcing row- and column-level access. An automated DAX-to-Metric-View transpiler parses semantic models, maps tables, generates draft definitions, flags non-automatable measures, and reports conversion gaps. The documented playbook combines gold-layer curation, KPI validation, regression testing, Genie optimization, persona agents, and Databricks Apps; the pilot reports AI answers aligned with established KPI definitions and BI reporting logic. ### [Ingesting the Milky Way: Petabyte-Scale with Zerobus Ingest](https://yomu.fyi/post/ingesting-the-milky-way-petabyte-scale-with-zerobus-ingest.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Aleksandar Tomić, Victoria Bukta, Nikola Obradović, Danilo Najkov, Branko Grbić, Milos Milovanovic - Published: Jun 11, 2026 The post benchmarks Zerobus Ingest, a managed, serverless, push-based service that writes producer data directly to Delta tables governed by Unity Catalog, against a petabyte-scale telemetry workload. Using NASA’s NEOWISE dataset and Locust, the test modeled fan-in from 2,048 concurrent streams, using Protocol Buffer 2 data over approximately 24 hours. The design replaces static partition-based ordering with stream-connection ordering, allowing heuristic routing across pods, dynamic partitioning, and autoscaling while existing streams drain. It also uses zeroparser, a zero-copy protobuf decoder whose design relies on Rust’s lifetime system and supports dynamic descriptors at about 1 GB/s per CPU core. The test sustained 12 GB/s to one table, ingested 1.04 trillion rows, and reached 1 petabyte within 24 hours; Zerobus Ingest is generally available, with additional APIs on its roadmap. ### [How ERGO Hestia reduced time-to-market with Databricks Lakebase and Model Serving](https://yomu.fyi/post/how-ergo-hestia-reduced-time-to-market-with-databricks-lakebase-and-mo.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Klaudia Ratkowska, Maciej Majewski, Oliver Börner, Alexander Migunov - Published: Jun 11, 2026 ERGO Hestia redesigned its real-time pricing platform to reduce deployment friction across more than 100 models and 1,000 variables while preparing B2C capabilities. Previously, processed data moved from Databricks through extraction jobs, external Azure PostgreSQL, and a custom caching adapter, creating governance overhead, deployment coordination, and latency spikes during large refreshes. The new architecture uses Lakebase Sync Tables as an online serving layer and Databricks Model Serving Endpoints, keeping data, request logic, and model serving within the lakehouse; Unity Catalog supplies lineage, version tracking, access controls, and audit trails. An incremental migration started with a low-criticality endpoint, measuring 20ms latency and less than 5% CPU utilization at 40 requests per second, before expanding toward larger workloads and the planned decommissioning of PostgreSQL. ### [Azure Databricks at Data + AI Summit 2026 featuring Industry Leaders and Partners](https://yomu.fyi/post/azure-databricks-at-data-ai-summit-2026-featuring-industry-leaders-and.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Kiriana Stukas - Published: Jun 11, 2026 Data + AI Summit 2026 brings Databricks and Microsoft leaders, partners, and customers together June 15–18, with in-person and virtual programming focused on Azure Databricks. The collaboration presents Azure Databricks as a first-party Microsoft offering for unifying data, analytics, and AI on a secure, scalable foundation, with sessions covering ecosystem integration, federated analytics, governance, modernization, and AI applications. One technical example introduces zero-copy federation between Azure Data Manager for Energy and Databricks compute, preserving ADME as the source of truth while avoiding large-scale data copies. Another shows Unity Catalog External Locations extending governed access to Microsoft OneLake without ETL pipelines, while customer sessions describe Apache Iceberg and Apache Spark integration, fragmented data consolidation, and production-grade finance workflows using Azure Document Intelligence. ### [Empower your healthcare agents with ready-to-use MCP on Databricks Marketplace](https://yomu.fyi/post/empower-your-healthcare-agents-with-ready-to-use-mcp-on-databricks-mar.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Yen Low, Mark Lee, Matthew Giglia, Nicholas Siebenlist, Jay Bhankharia, Paul Ford, Itai Weiss - Published: Jun 11, 2026 Databricks announces ready-to-use Model Context Protocol (MCP) servers for healthcare and life sciences through Databricks Marketplace, addressing the need to combine curated biomedical knowledge, timely data, specialized tools, and private records. Listings include services for drug and target intelligence, literature, clinical trials, FDA information, Medicare coverage, ontologies, real-world evidence, clinical semantics, and interoperability, while Climb connects live public sources with private Gold-layer data under Unity Catalog governance. Marketplace and custom MCP servers are centralized in the MCP Catalog and governed by Unity AI Gateway, with Genie Spaces, AI Search, Unity Catalog functions, and SQL Warehouses available as managed MCP servers. Users can assemble agents in AI Playground, Agent Bricks, or notebooks, then deploy endpoints or apps with MLflow tracing, evaluation, human feedback, and AI guardrails; examples span molecular-property lookup, clinical questions, and drug research. [Newer posts](https://yomu.fyi/topic/unity-catalog/page/2.md) · [Older posts](https://yomu.fyi/topic/unity-catalog/page/4.md)