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135 posts about Unity Catalog. Every summary links to the original.
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An agentic ad stack wires governed customer signals to campaign execution through a shared tool interface and server-side guardrails. The diagram shows each layer, the direction of a tool call, and where control stays human.
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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.
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.
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.
AWS and Databricks describe their expanded collaboration at Data + AI Summit 2026, where AWS returns as a Legend Sponsor with sessions, demos, customer stories, and industry forums. The partnership centers on generative AI adoption, unified governance, and open data architectures, including an agentic stack that combines Amazon Bedrock, Bedrock AgentCore, Kiro, and the Databricks Data + AI Platform. A featured integration uses a governed MCP connection through Databricks Apps so AgentCore can query Unity Catalog-governed data, ask AI/BI Genie questions, and read low-latency state from Lakebase while honoring existing permissions. AWS will demonstrate these workflows at Booth #100 and present a session on federating Unity Catalog to AWS Glue, alongside customer examples including Mastercard, Talkdesk, nCino, Addepar, and Workday. Attendees can also join technical conversations, receptions, and a 14-day Databricks on AWS Marketplace trial with $400 in usage credits.
Databricks has announced the public preview of Custom URLs, giving each account a single branded domain that serves its workspaces. Previously, workspace-specific URLs made navigation, sharing, bookmarking, and account-wide features more cumbersome, while users had to log in repeatedly when switching workspaces. Custom URLs use a shared account session to verify access and create workspace sessions seamlessly, while preserving authorization boundaries and keeping existing per-workspace URLs functional. The feature provides a unified Genie entry point, cross-workspace Unity Catalog lineage, and an account-level URL that remains stable during disaster-recovery failover, including for downstream tools using existing connection strings. Activation requires Unified Login; Frontend Private Link workspaces fall back to per-workspace URLs, and account admins can claim a URL, enable it, and optionally turn on automatic redirects.
Rivian is building electric vehicles and services that require fast, trusted decisions across manufacturing, supply chain, finance, service and operational planning, while business users need reliable metrics and insights. Using Databricks AI/BI, Genie, Unity Catalog metric views, Databricks Apps and AI-assisted engineering, the company is consolidating dashboards, semantic definitions, permissions, sensitive data and AI-powered workflows on one governed foundation. Rivian migrated a massive multi-domain dashboard base in less than six months, is standardizing more than 50 metrics, and worked with Databricks as a design partner on roughly 58 product features. The resulting self-service analytics and operational applications cut supply-chain monitoring time by 60 to 70%, reduce inventory investigations from over 30 minutes to under two, predict stock-out risk more than four days ahead, and reduce some ingestion setup time by more than 60%, supporting AI-powered decisions without competing versions of the truth.
Databricks is publishing a public library of 40 Lakehouse industry data models designed to provide Silver-layer foundations for analytics and machine learning. Each industry offers a Minimum Viable Model and Expanded Coverage Model derived from the same model.json, with breadth rather than attribute depth distinguishing the scopes. A rules-driven AI agent applies more than 200 structural checks across 14-plus modeling domains, enforcing hierarchy, primary and foreign keys, normalization, division balance, data types, governance tags, and acyclic relationships. The models deploy to Unity Catalog in three physical cataloging styles and include DDL, schemas, metric views, classification tags, ontology, diagrams, and synthetic data with valid references. The airline ECM example contains 19 domains, 420 products, 17,278 attributes, 420 primary keys, and 2,877 foreign keys, while the models remain customizable starting points requiring domain expertise and organizational review.
Databricks announces a Software-Defined Storage (SDS) Ecosystem for governing enterprise data across on-premises, private-cloud, and edge environments without requiring migration. It responds to sovereignty and regulatory constraints, data-gravity economics, latency requirements, and the need to unlock AI value in backup, archive, and other previously inaccessible data. Storage partners implement the open-source OpenSharing protocol, expose an endpoint, and connect it to Unity Catalog so Databricks Serverless Compute can access governed data where it resides. The integrations provide a unified catalog and support Serverless Compute, Genie, AgentBricks, and model training with zero data movement or duplication; MinIO is generally available, while Everpure, Qumulo, and VAST Data are in preview stages. Databricks also says commitments are in place from six additional providers and that Volumes APIs are being developed to extend OpenSharing to unstructured files for generative-AI workloads.
AML operations are strained by fragmented systems, high false-positive volumes, manual case documentation, and opaque vendor scoring, leaving analysts focused on backlog rather than financial-crime intelligence. The proposed Databricks Data + AI Platform unifies transaction monitoring, KYC, sanctions, case history, and policy data under Unity Catalog, using Lakeflow Connect and a Bronze–Silver–Gold Delta architecture with masking, row-level security, and lineage. MLflow, Model Serving, Lakehouse Monitoring, and inference tables support institution-specific detection models, while Agent Bricks coordinates agents for evidence gathering, recommendations, and SAR drafting with analysts retaining final decisions. The architecture also uses Lakebase for governed operational state and Databricks Apps for analyst and executive experiences. Reported outcomes include a 75% reduction in false positives reaching the analyst queue and compressing three-to-six-hour investigations to minutes.
Claude Fable 5 is now generally available on Databricks, with rollout across AWS, Azure, and Google Cloud through Unity AI Gateway. The Mythos-class model targets long-running, complex, and ambiguous work, including autonomous enterprise workflows, document question answering, code investigation, and multimodal tasks. In Databricks' OfficeQA Pro benchmark, Fable 5 achieved 57.9% correctness, setting a state of the art; compared with Claude Opus 4.8, it was 20% more accurate and used 12% fewer tool calls, but ran approximately 30% slower and generated 2.5x more output tokens. Unity AI Gateway provides unified API access, fine-grained permissions, Unity Catalog logging, request and tool-call guardrails, and spend controls. Agent Bricks supports domain-specific agents, while Anthropic's policy includes 30-day retention for trust and safety purposes only.
The 2026 Databricks Customer Awards recognize organizations and leaders using the Databricks Data + AI Platform across eight categories and four regions. The announcement names winners including Applied Materials, Virgin Atlantic, Fonterra Co-operative Group, Telefónica | Vivo, Virtue Foundation, Octopus Energy, Axpo, Atlassian, Wassym Bensaid at Rivian and Volkswagen Group Technologies, and Kenan Colson at Lippert. Examples include Applied Materials’ move from a Hadoop-based data lake to a governed lakehouse, with 1,500-plus analysts, more than 100 production machine-learning models and faster pipeline development, while Fonterra centralized data to improve supply-chain and compliance work. Rivian unified vehicle, factory and enterprise data, consolidated legacy systems into Delta and Unity Catalog, and designed for 500–600 petabytes; Lippert deployed AI tools across customer care, finance and HR. The announcement presents data and AI adoption as a cross-functional operating model.
AI-assisted development can produce unreliable SQL and models when an assistant guesses schema details or exceeds the user's data access. Kiro can connect to Databricks through Model Context Protocol (MCP) in two ways: four Databricks-managed remote servers for Genie, SQL, Unity Catalog Functions, and Vector Search, or the Databricks AI Dev Kit Power, which installs a local Python MCP server and broader skills. The AI Dev Kit now supports Kiro through its unified installer, while the Power provides one-click onboarding with authentication detection and skill loading. Both paths use Unity Catalog permissions, including row-, column-, and tag-based grants, so the assistant sees the user's effective access; Path A uses token-based configuration, while Path B supports OAuth U2M, OAuth M2M, profiles, or PATs. Examples show schema-grounded SQL, dbt joins using real columns, query comparisons, lineage checks, and generation of Databricks jobs or Asset Bundles.
Databricks Genie is presented as a cross-industry technology layer for enterprise challenges including financial planning, legal compliance, and IT operations. As a “Research Agent,” it can generate multi-step research plans to explain business anomalies and support answers with verifiable proof from the lakehouse. The post showcases partner solutions across technology, sales, marketing, HR, finance and procurement, supply chain, customer service, and IT operations, with examples spanning governed analytics, multi-agent orchestration, data observability, causal analysis, and incident management. These implementations aim to replace fragmented or static workflows with real-time, contextualized intelligence and production-grade agentic workflows, supporting anomaly investigation, root-cause analysis, ticket classification, and conversational troubleshooting. The stated goal is faster, more confident decision making across departments through governed self-service access to insights.
Cross-engine ABAC is announced in Beta, extending Unity Catalog's fine-grained governance to external engines through Iceberg REST Catalog APIs. It supports tag-based row filters and column masks, including conditional logic and SQL UDFs, while allowing policies to be defined once and enforced across engines. For an external query, the engine sends a scan request, Unity Catalog evaluates entitlements and applicable policies, and returns a filtered scan plan before the engine processes authorized files. Enforcement remains at the catalog layer, so engines need not implement governance logic and can use the open scan APIs. Apache Spark is supported today through Iceberg-Spark and Delta-Spark connectors, with Starburst and DuckDB integrations coming soon; the Beta also points toward Apache Iceberg label exchange for future governance metadata sharing.
Databricks announces a broad set of Apache Iceberg capabilities in Unity Catalog, spanning General Availability, previews, and beta releases. Managed Iceberg is GA, supporting table creation, reads, writes, optimization, governance, and sharing, while Iceberg v3 adds deletion vectors, row tracking, and VARIANT across managed, foreign, and UniForm-enabled tables. Unity Catalog also federates external catalogs, vends credentials, shares live data with Iceberg REST-compatible clients through Delta Sharing, and applies attribute-based access control during server-side scan planning for supported external engines. These capabilities are presented as a unified approach to open APIs, cross-engine governance, zero-copy sharing, and production performance without copying data. The post also outlines Iceberg v4 and a proposal for Delta 5.0 to adopt an adaptive metadata tree structure.
BI dashboards can become slow and expensive when teams respond to latency with separate aggregate tables, refresh pipelines, extracts, and tool-specific semantic layers. The post presents Databricks’ BI serving stack from physical storage through Unity Catalog’s governed semantic layer, recommending Gold-layer star schemas, managed tables, liquid clustering, and Predictive Optimization to reduce scanned data and improve query plans. Metric Views centralize KPI definitions and semantic metadata for dashboards, Genie, SQL notebooks, third-party BI tools, and AI agents, while materialization automatically maintains incremental pre-aggregations and routes queries transparently. Additional TCO guidance covers serverless SQL warehouse autoscaling, DBSQL disk and query-result caching, direct lakehouse connections, and system-table monitoring. The stated outcome is compounded lower latency and compute cost, including an observed average 22% performance improvement from Predictive Optimization and sub-second performance from materialized metrics.
Lakebase Change Data Feed (CDF) is available in Public Preview to reduce the manual effort of moving data from operational databases into downstream systems. The feed is enabled once for all tables in a project, stored and governed in Unity Catalog Managed Tables, and readable by engines, models, and agents without separate extraction pipelines. From one shared feed, teams can build streaming pipelines with SDP, create materialized views with DBSQL, or compute and store embeddings with Agent Bricks, while consumers remain isolated from the primary operational workload. The announcement positions Lakebase as the native Bronze layer in a medallion architecture, complementing Synced Tables and providing governance and lineage across the data lifecycle.
Telecommunications companies are adopting AI for customer experience, network operations, and cost reduction, yet initiatives often stall before production because fragmented, ungoverned, semantically opaque data creates data debt. The post argues that AI readiness depends on a semantic layer unifying datasets and business definitions, governance, and catalog metadata across systems such as Oracle, Snowflake, Salesforce, ServiceNow, and Databricks. It presents Unity Catalog as the proposed foundation, using Delta Sharing, Lakeflow Connectors, and Lakehouse Federation to exchange, ingest, or query data without uniformly replicating it, while privilege-aware metadata and audit logging support compliance. Metric Views, lineage, tags, and glossaries give agents authoritative meanings for measures and terms such as revenue, ARPU, active user, and FTTH. The conclusion is that trustworthy operational AI requires a governed, unified data foundation and organizational commitment, not simply more capable models.
Databricks supports writing OpenTelemetry (OTel) traces directly to Unity Catalog, where real-time telemetry is stored in Delta tables for governed analytics and retention. AI traces capture prompts, tool calls, responses, latency, and execution paths, enabling debugging, evaluation, and monitoring, while lakehouse storage also allows SQL queries, dashboards, joins with business data, and PII controls. The managed, serverless ingestion layer uses Zerobus Ingest to accept OTLP over gRPC from collectors and REST integrations, streaming spans, logs, and metrics to Unity Catalog without intermediate message buses. A LangGraph support manager assistant demonstrates instrumentation with mlflow.langchain.autolog() and an @MLflow.trace root span, while Genie is invoked through MCP for data-driven questions. The resulting traces can be searched in MLflow, evaluated at scale, and monitored continuously, with FAQ details stating 200 QPS starting throughput, no storage limit, and Unity Catalog governance options for access control, masking, and filtering.
Financial services organizations have built sophisticated lakehouses, streaming pipelines, model-serving infrastructure, and self-service BI, but access remains concentrated among technical teams. Business leaders still often rely on analysts because they may lack SQL skills, BI training, or analyst access, creating the “last mile” of data democratization. Databricks Genie addresses this gap through a conversational AI interface that converts plain-English questions into governed SQL queries executed against the Databricks Lakehouse without an analyst in the loop. It operates within Unity Catalog access policies, restricts users to authorized data, makes queries read-only, and logs interactions for audit purposes, while its semantic layer maps organizational terminology such as NIM, LTV, and NII to the organization’s meanings. The stated outcome is faster, auditable answers for business questions and usage data that can inform data-product priorities.
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