---
title: "Unity Catalog"
description: "137 posts about Unity Catalog, summarised, each linking to the original."
---

# Unity Catalog
> 137 posts about Unity Catalog, summarised, each linking to the original.

## Articles

### [Stripe data now available on Databricks via Databricks Marketplace](https://yomu.fyi/post/stripe-data-now-available-on-databricks-via-databricks-marketplace.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Justin Fenton, Harish Gaur, Matt Napoli
- Published: Apr 29, 2026
- Type: Announcement

Stripe Data Pipeline is now available on Databricks Marketplace through Delta Sharing, allowing payment and business data to be analyzed in a Databricks workspace without ETL. Stripe data—including transaction records, customer histories, subscriptions, refunds, and payouts—remains in Stripe’s infrastructure and is queried directly through Unity Catalog, where it can be joined with other tables. The approach replaces polling integrations and custom jobs, avoiding per-call charges, connector licensing fees, data duplication, and maintenance code while providing row- and column-level access controls, audit trails, and compliance features. The shared data is positioned for AI-native uses such as continuous transaction monitoring, churn scoring, LLM-powered retention workflows, Genie queries, and finance analytics apps. Getting started involves visiting the Stripe Data Pipeline listing on Databricks Marketplace.


### [Interoperability between Unity Catalog and Google BigQuery via catalog federation](https://yomu.fyi/post/interoperability-between-unity-catalog-and-google-bigquery-via-catalog.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: John Spencer, Jason Reid
- Published: Apr 29, 2026
- Type: Announcement

Databricks and Google Cloud announce bidirectional catalog federation that lets customers access the same data from Unity Catalog or BigQuery without duplication. The interoperability uses Apache Iceberg REST catalog APIs, enabling both platforms to read and write the same tables through a common open interface. Google Cloud's catalog federation is in preview for reading Unity Catalog-managed tables from BigQuery, while Google Cloud's Lakehouse federation is in private preview for Databricks users to govern and read foreign Iceberg tables. Unity Catalog's policies, fine-grained access controls, and lineage tracking apply to federated data, and users can query it with Genie using natural language. The announcement positions open standards as a path to interoperability across data platforms and previews future catalog metadata exchange through Iceberg REST catalog APIs.


### [Operationalizing AI for public sector fraud prevention](https://yomu.fyi/post/operationalizing-ai-for-public-sector-fraud-prevention.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Johnathan Tafoya, Kacey Hertan
- Published: Apr 28, 2026
- Type: Explainer

Public-sector agencies face fraud methods including synthetic identities, deepfake-enhanced documentation, and personalized social engineering, while legacy controls remain fragmented and difficult to scale. The fictional Services Bureau demonstrates an operating model that combines Databricks Apps, Lakebase, Unity Catalog, Delta Sharing, Agent Bricks, and AI/BI Genie in a single fraud-operations environment. Governed data lands in Delta tables; Unity Catalog applies attribute-based access control, masks PII by role, and provides lineage, while agents connect live lakehouse queries, agency policies, and external fraud signals through MCP. Analysts review evidence and recommendations, then approve, override, or escalate cases, keeping human judgment central. The described workflow turns weeks of manual investigation into a day, supports dashboards and conversational SQL, and is presented as making fraud decisions faster, more secure, transparent, and defensible.


### [Agentic data engineering with Genie Code and Lakeflow](https://yomu.fyi/post/agentic-data-engineering-with-genie-code-and-lakeflow.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Gal Oshri, Camiel Steenstra, Lennart Kats, Joanna Zouhour
- Published: Apr 28, 2026
- Type: Explainer

Genie Code is presented as a natural-language assistant for developing, orchestrating, deploying, and debugging data pipelines and jobs. It uses pipeline and job context, including code, configuration, run results, Unity Catalog metadata, lineage, popularity, and code samples, to help engineers discover datasets and understand data flows. Engineers can describe pipelines or jobs, and Genie Code can generate Spark Declarative Pipelines with Bronze, Silver, and Gold layers, sources, transformations, data quality expectations, and outputs, then configure orchestration, schedules, dependencies, Auto Loader, AutoCDC flows, and Declarative Automation Bundles. It also analyzes failures and unexpected row-count or schema changes, proposes cross-file updates with reviewable diffs, and supports extensions through custom instructions, agent skills, and MCP servers. The stated result is faster development and guided debugging while workflows remain aligned with Unity Catalog governance, performance, and data quality standards; future plans include background failure response and cluster right-sizing.


### [Securely send first-party conversion signals with Snapchat Conversions API on Databricks Marketplace](https://yomu.fyi/post/securely-send-first-party-conversion-signals-with-snapchat-conversions.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Nikhil Gaekwad, Jen McNamee, Katy Yuan
- Published: Apr 28, 2026
- Type: Announcement

Snapchat Conversions API is now available as a Databricks Marketplace listing that lets teams activate first-party conversion signals directly from governed Lakehouse data. The included pre-built notebook reads gold-layer tables, batches web, app, and offline events, and supports deduplication with Snap Pixel, allowing server-side signals without double-counting. Because it runs in the Databricks environment, Unity Catalog preserves governance, lineage, security, and access controls while teams control what data is shared and when. The post says fuller server-side coverage can raise Event Match Quality, while citing that advertisers who improved their EQS from “Poor” to “Good” saw 26% higher ROAS and 49% lower CPI. It also describes fresher custom audiences and Lookalikes plus more complete deduplicated measurement.


### [The next generation of Databricks Genie](https://yomu.fyi/post/the-next-generation-of-databricks-genie.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Ken Wong, Dillon Morrison, Richard Tomlinson
- Published: Apr 26, 2026
- Type: Announcement

The next generation of Databricks Genie expands the assistant beyond individual Genie Spaces, combining structured and unstructured enterprise data in a unified chat experience. It reuses logic from certified Genie Spaces, governed dashboards, and Databricks Apps, with metadata-based routing that prioritizes higher-trust sources, while new reasoning models and agent architecture handle questions spanning multiple domains. Built-in connectors for Google Drive and SharePoint, plus MCP support, let Genie access knowledge stores and take actions; Unity Catalog AI Gateway manages these connections. The experience replaces Databricks One as an account-level global home, adding domains, custom URLs, unified login, automated identity management, and governance through Unity Catalog. Native iOS and Android apps extend access to dashboards, apps, and chat beyond desktop environments.


### [Model risk management in 2026: A banker's guide to the revised interagency guidance](https://yomu.fyi/post/model-risk-management-in-2026-a-banker-s-guide-to-the-revised-interage.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Pavithra Rao, Jennifer Miller, Chaitanya Varanasi, Kim Hatton
- Published: Apr 25, 2026
- Type: Problem & solution

The post presents the April 17, 2026 revision to model risk management guidance as a shift toward risk-based, principles-driven oversight, replacing several earlier issuances from the Federal Reserve, FDIC, and OCC. It identifies five operational changes: materiality tiering, end-to-end lifecycle governance, reproducible effective challenge, continuous drift and performance monitoring, and application of MRM principles to GenAI and agentic systems. Its proposed response is a Databricks reference architecture built on Unity Catalog, Delta Lake, Lakeflow Declarative Pipelines, Feature Store, MLflow, Model Registry, Model Serving, and assurance tools. The design makes tiering metadata-driven and turns lineage, validation, approvals, monitoring, documentation, and retirement records into evidence generated during normal model work. The stated conclusion is that a unified substrate can reduce integration and manual evidence work, allowing regulatory changes to become configuration exercises rather than multi-quarter programs.


### [Announcing the Public Preview of Lakeflow Designer](https://yomu.fyi/post/announcing-the-public-preview-of-lakeflow-designer.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Jason Messer, Emanuel Zgraggen, V Maharajh, Matt Jones, Tracy Yang
- Published: Apr 23, 2026
- Type: Announcement

Databricks announces the Public Preview of Lakeflow Designer, a visual, no-code, AI-native experience for data preparation and analytics built directly into the Databricks platform. It targets analysts, domain experts, and other less technical users with drag-and-drop workflows and natural-language authoring, while representing each transformation as an operator with previews. Genie Code can use Unity Catalog metadata, descriptions, lineage, popularity, and example queries to generate or modify workflows, inspect transformation results, and iterate when needed. Every visual transformation produces production-ready Python code that can be reviewed, versioned in Git, and scheduled through Lakeflow Jobs, while users pay for compute rather than per-user licenses. The preview is available in all workspaces, although an administrator may need to enable Visual data prep in the preview portal.


### [How to transform document activation workflows with Genie and Agent Bricks](https://yomu.fyi/post/how-to-transform-document-activation-workflows-with-genie-and-agent-br.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Elena Tesser
- Published: Apr 22, 2026
- Type: Explainer

Enterprise document workflows often require people to read PDFs, copy fields into spreadsheets, and re-enter data into business systems, creating delays, error risk, and weak auditability. The proposed alternative combines AI/BI Genie for natural-language queries over Delta tables, Agent Bricks for production agents, and Unity Catalog for permissions, lineage, and traceability. A five-phase workflow extracts fields into Lakeflow Bronze, Silver, and Gold layers, then supports structured queries, clause-level RAG answers, multi-agent routing, and actions through MCP servers. Examples include syncing validated rights data to ERP or CRM systems, updating billing entitlements, and triggering workflows from detected obligations. The approach targets media, advertising, ad tech, and telecommunications, where customers see improvements including faster month-end close, recovered revenue, reduced leakage, and lower operational risk.


### [Beyond the spreadsheet: How Databricks is delivering the modern CFO in financial services](https://yomu.fyi/post/beyond-the-spreadsheet-how-databricks-is-delivering-the-modern-cfo-in.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Jennifer Miller, Marcela Granados, Andrea DeSosa, Alex Oberlander, Kim Hatton, Pavithra Rao, Naeem Rehman, Pravin Varma, Olga Deriy, Prasanna Selvaraj
- Published: Apr 22, 2026
- Type: Explainer

Financial-services CFOs are being asked to act as Strategists and Catalysts, but fragmented legacy systems, T+1 batch processing, opaque reporting logic, and mismatched business semantics keep them focused on stewardship and retrospective operations. The proposed answer is Databricks as a unified, governed platform combining real-time streaming, centralized lineage, data and AI, with Unity Catalog, Lakeflow, Genie, and Agent Bricks addressing trust, latency, access, and model reproducibility. Unity Catalog can connect semantic definitions and end-to-end lineage from transactions through regulatory reports and models, while Lakeflow supports continuous ledger and liquidity processing. The post cites a global bank reducing liquidity-reporting processing from 10 hours to 8 minutes and Nationwide Insurance reporting a 5-point combined-ratio improvement and 3-point expense-ratio improvement. It presents a Lakehouse-based CFO stack as a shift from reporting historical results toward real-time capital management and previews AI-driven deposit and PPNR modeling.


### [Multimodal Data Integration: Production Architectures for Healthcare AI](https://yomu.fyi/post/multimodal-data-integration-production-architectures-for-healthcare-ai.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Maks Khomutskyi
- Published: Apr 22, 2026
- Type: Problem & solution

Production healthcare AI often depends on combining genomics, imaging, clinical notes, and wearables, but separate modality stacks can leave initiatives stalled before deployment. A lakehouse pattern addresses this by landing each modality in governed Delta tables under Unity Catalog controls while supporting cross-modal features, lineage, time travel, and reproducible training sets. Genomics processing can use Glow on Spark, imaging embeddings can feed governed tables and vector search, notes can become temporally aware entities, and wearables can flow through Lakeflow Spark Declarative Pipelines for streaming feature windows. The post compares early, intermediate, late, and attention-based fusion, matching them to dimensionality, temporal, and availability constraints, with late fusion presented as a practical baseline when modalities are missing. It argues that modality masking, sparse attention, and transfer learning are recommended responses because incomplete data is the default and connects the architecture to governed tumor board review, faster cohort assembly, and more clinical workflows.


### [Introducing the Databricks Excel add-in for business users](https://yomu.fyi/post/introducing-the-databricks-excel-add-in-for-business-users.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Toussaint Webb, Kaitlin Baumgardner
- Published: Apr 21, 2026
- Type: Announcement

Databricks has introduced the Excel Add-in, now in public preview, to let business users import and analyze lakehouse data directly in Excel without writing SQL or configuring ODBC drivers. Built on Databricks SQL and Unity Catalog, it provides point-and-click access to tables and metric views, native Excel pivot tables, refreshable results, reusable Workspace queries, and optional saved SQL. Metric views let data teams define business semantics once while Unity Catalog centrally manages permissions and controls, helping avoid duplicated logic and metric drift. The preview supports selecting tables and metric views, parameterized custom functions, manual refreshes, pivot-table filtering, query reuse, and workspace changes; scheduled refresh, AI integrations, and other usability improvements are planned next.


### [Mercedes-Benz builds a cross-cloud data mesh with Delta Sharing and intelligent replication, cutting costs by 66%](https://yomu.fyi/post/mercedes-benz-builds-a-cross-cloud-data-mesh-with-delta-sharing-and-in.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Alexander Summa, Aleksandar Dragojevic
- Published: Apr 20, 2026
- Type: Problem & solution

Mercedes-Benz built a cross-cloud data mesh to share after-sales data between AWS and Azure while supporting its data-defined vehicle strategy. Existing direct queries incurred egress costs, and weekly full loads delayed updates for datasets that included vehicle over-the-air events and workshop visits; source tables were in Iceberg while many consumers expected Delta. Using Unity Catalog and Delta Sharing, the company federated AWS Glue tables, established cross-metastore shares, and used periodic serverless Sync Jobs with Delta Deep Clone to incrementally replicate selected tables into Azure object storage for local querying. This hybrid model preserves direct sharing for fresher workloads while reducing transfer for less time-sensitive ones. For the initial ten data products, egress costs fell 66%, update frequency improved from weekly to every second day, and the source reports an approximately 93% annual reduction in its 50-use-case comparison.


### [Introducing Genie Agent Mode](https://yomu.fyi/post/introducing-genie-agent-mode.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Hanlin Sun
- Published: Apr 17, 2026
- Type: Announcement

Databricks introduces Agent mode in Genie spaces, an agentic process that plans, explores, and reasons over data to answer business questions. For each question, Genie can confirm an issue, test potential contributors through multiple queries, reflect on results, and decide what to investigate next, using Unity Catalog metadata and author-defined semantics from the Genie space. It produces a findings report with quantified results, identified contributors, visualizations, and references to the underlying SQL, and may include actionable recommendations. The agent scales its reasoning to task complexity, using validation for straightforward questions and more planning for multi-step investigations; in AI/BI Dashboards, Genie uses Agent mode by default. Workspace admins enable the feature in Workspace Previews, after which users turn on the Agent toggle in Genie spaces; API support and unstructured document analysis are planned additions.


### [Banks don't have an AI problem — they have a data platform problem](https://yomu.fyi/post/banks-don-t-have-an-ai-problem-they-have-a-data-platform-problem.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Naeem Rehman, Jennifer Miller
- Published: Apr 17, 2026
- Type: Explainer

The post argues that banks’ difficulty scaling AI stems less from model capability than from fragmented data, weak governance, and limited real-time access. Examples from CBA Live 2026 include model drift in credit scoring, fragmented customer signals, and a collections model that predicted, with 85% accuracy, how many days newly delinquent accounts would take to cure using governed data. It also presents Erica’s 3.2 billion interactions since 2018 as evidence that production AI requires continuous tuning, monitoring, and edge-case management, while generic models decay in frontline settings. The proposed Databricks architecture combines Lakehouse, Unity Catalog, Lakeflow, Lakebase, MLflow, Model Monitoring, Online Feature Store, Genie, and Agent Bricks to support governed analytics, low-latency decisions, auditable models, and constrained agent actions. Its conclusion is that a shared data platform should precede additional AI use cases because it improves deployment speed, trust, explainability, and regulatory defensibility.


### [Why your agents can't read enterprise documents — and how to fix it](https://yomu.fyi/post/why-your-agents-can-t-read-enterprise-documents-and-how-to-fix-it.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Archika Dogra, Sergei Tsarev, Erich Elsen
- Published: Apr 16, 2026
- Type: Announcement

Enterprise agents often reason well over clean text but struggle with scanned PDFs, inconsistent layouts, nested tables, images, and handwriting, making document processing an accuracy ceiling for business workflows. The post announces Document Intelligence, a pipeline of chainable AI Functions: ai\_parse\_document converts scans into layout-enriched structured text, while ai\_classify and ai\_extract support routing and entity extraction. On OfficeQA treasury-bond documents, preprocessing with ai\_parse\_document produced a 16% average performance gain across tested agent frameworks without changing their reasoning harness. Across invoice, contract, medical-note, and financial-filing extraction tasks, it reportedly delivered the highest accuracy at 5–7x lower cost than comparable pipelines, with serverless batch infrastructure scaling the same SQL call from 100 to 100,000. Databricks integrates ingestion, orchestration, governance, and agent building through Lakeflow Connect, Lakeflow Jobs or Spark Declarative Pipelines, Unity Catalog, and Agent Bricks, aiming to replace fragmented services with reusable workflows.


### [Building with Databricks Document Intelligence and Lakeflow](https://yomu.fyi/post/building-with-databricks-document-intelligence-and-lakeflow.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Giselle Goicochea, Joanna Zouhour
- Published: Apr 16, 2026
- Type: Tutorial

The post presents a production-oriented approach to Intelligent Document Processing (IDP) that brings PDFs, images, and office documents into Databricks’ governed data lifecycle. Lakeflow Connect ingests files from sources such as SharePoint and Google Drive into Unity Catalog Volumes and tables, applying access control, lineage, and auditing while supporting incremental reads and writes. Databricks’ Document Intelligence then uses ai\_parse\_document to preserve structure in VARIANT output, with ai\_extract, ai\_classify, and ai\_prep\_search available for enrichment, routing, and retrieval preparation. Lakeflow Jobs productionizes the workflow with orchestration, control flow, retries, triggers, serverless scaling, and observability across ingestion, parsing, and serving tasks. The post also recommends joining extracted data to master data, using service principals, and monitoring drift and extraction accuracy, presenting the combined platform as a way to create trusted, queryable datasets from unstructured content.


[Newer posts](https://yomu.fyi/topic/unity-catalog/page/6.md)