# Learning Palantir Foundry 🚀 "Sales reps see only the rows for their assigned customers" — achieved on a single dataset, without spawning a copy per department. That is what Restricted Views (row-level security) deliver. 📌 Title and Feature URL Title: 制限付きビュー(行レベルセキュリティ) URL: 📝 Overview Restricted Views implement granular row-level access control. A restricted view is built on top of a backing dataset and lets different users see different subsets of the same underlying data based on defined permissions. This removes the need to copy datasets per team: you keep one dataset as the source of truth shared across the company while splitting visibility at the row level. 🔧 How It Works Restricted views operate through policies containing rules that determine row visibility. - Policies evaluate the viewing user's attributes, column names from the backing dataset, and specific values (strings, Booleans, numbers, arrays) to decide which rows are shown. - When referencing users, groups, or organizations, you must use the unique identifier (UUID) in both the policy column and the policy definition — names alone will not work. - In marking-backed views, the upstream dataset holds a STRING ARRAY column of Marking IDs, and each row is visible only to users with the required markings. - A restricted view is built on top of a backing dataset and cannot be used as an input for transforms. - Experimental branching support allows adding and merging restricted view policy changes. 🛠 Practical Usage - Add a column to the backing dataset that drives row access (for example, assigned branch or organization ID). - Define a policy that matches that column against user attributes to build the row-level filter. - Save restricted views in a separate Project from the source datasets to keep access management clean. - For markings, attach an array of required Marking IDs per row to control visibility. 🎯 Use Cases - Limit sales representatives to viewing customers at their assigned branch. - Separate records on a shared table by department or organization. - Disclose differently classified records only to users holding the required markings. ⚠️ Caveats - Restricted views cannot serve as transform inputs, so they cannot be plugged directly into downstream pipeline processing. - Users, groups, and organizations must be referenced by UUID; name-based references do not work. - Merging policy changes via branching is experimental and may not be universally available. #PalantirFoundry# #DataGovernance#

Governance used to mean slower data and AI initiatives. Not at @BookingCom With our integrated data governance, teams have a trusted foundation to find, access, and use fully compliant data faster.

Circle Foundation is supporting Pacific Community Ventures with a grant for the Radiant Data Hub, an AI-enabled platform bringing data governance, predictive modeling, and impact tools to community lenders.

Databricks Chief Information Officer Naveen Zutshi has been named to TIME's 2026 Executives of the Year: Tech & Data list! @TIME highlights Naveen's mission to make every Databricks employee a builder with Genie, while strengthening the data governance behind how Genie accesses and acts on company data. Congratulations to Naveen and all 50 leaders shaping the future of technology, AI, cybersecurity, and digital innovation. Explore the full list:

🚀 Alibaba Cloud is now in Brazil. We’ve launched our first cloud region in Brazil to accelerate cloud and AI transformation. With the new data centers, local infrastructure delivers secure, resilient and scalable cloud services with low latency and data governance for Brazilian businesses. And there’s more: we’re bringing enterprise-grade agentic AI services to help businesses build, deploy and operate AI agents at scale. 🌎 106 Availability Zones | 31 Regions worldwide Brazil’s cloud + AI transformation starts here. #AlibabaCloud# #CloudComputing# #AgenticAI# #AI# #Brazil#

Run inference over millions of records — free of SQL, and without your data ever leaving Snowflake. Here's distributed batch inference at scale ⚙️ Title: Batch Inference at Scale URL: ⚙️ Overview A capability that runs distributed inference workloads on Snowpark Container Services (SPCS) with Ray as the execution framework. Inference runs as a dedicated distributed workload, supporting both traditional models and LLMs, consolidating complex operations into a single API call. ❓ Challenges Solved Many customers, especially those migrating from non-SQL systems, need batch inference decoupled from SQL. ・This is especially true for files and unstructured data at large scale ・Rearchitecting workflows around SQL-first patterns is a heavy burden 💡 Methodology & How It Works ・The input DataFrame is materialized and written to a stage as Parquet files ・A job is provisioned on SPCS; the primary node initializes as the Ray head and replicas join as workers ・Each worker reads staged data, performs inference independently, and writes results to an output stage ・Unified API: a single run_batch() call handles both structured and unstructured data ・Multimodal support (images, audio, video); workers load weights once and reuse across batches; JobSpec controls workers and GPU allocation 🌍 Use Cases ・Nightly summarization of millions of support tickets ・Product catalog enrichment via image-to-text generation ・Information extraction from scanned PDFs, audio transcription and labeling, video classification and description BatchInferenceTask integrates with Snowflake Tasks for DAG automation, and all processing stays inside Snowflake — running large-scale inference while preserving data governance. #Snowflake# #BatchInference#

What if AI could figure out the right business context without waiting for a fully built ontology? Genie Ontology does that by inferring relevant context across your data and assets to answer open-ended questions. Advancing Analytics’ @MrSiWhiteley digs into how OntoRank chooses what to trust, why certified assets still matter, and what that means for data governance. Watch the full video:

Two enterprise AI failure modes, two evenings, one fall. Cost (Sept 29) and hallucinations (Oct 8) are usually treated as separate problems. They're not — bad data governance drives both. NYC, under 100 seats each. Sept 29th: Oct 8th:

Ready to move your AI initiatives from experimentation to real-world impact? #SnowflakeWorldTour# is coming to 23 cities worldwide with practical strategies for building, deploying, and governing AI directly on your data. Here's what's in store: 🔹 Hands-on sessions on Snowflake Cortex 🔹 Generative AI for the enterprise 🔹 Data governance in the age of AI 🔹 Customer stories showing what's possible 🔹 Connections with local data leaders and experts Discover new ways to turn your data into intelligent applications! Register today for a city near you! 📍

The AI slowdown might actually be exactly what we need. 👀 One of my biggest Dreamforce takeaways: the conversation is shifting from how fast we can build AI to how we actually get PEOPLE to adopt it. And Salesforce’s strategy is getting clearer: Slack = where the work happens
Surfaces = how we interact with AI
Claude = another way into your Salesforce ecosystem
Salesforce = the trusted data + governance layer underneath it all The technology moved fast. Now we need to bring our people with us. And THAT might be the most important part of the next phase of AI. #Dreamforce# #DF26# #Salesforce# #Slack# #AI# #Claude# #Slackforce# #TechTok# @Benioff @rseaman2 @rbgavin @slack @salesforce