Arjun Asokakumar, MMA, CHRL

When data agents fail, they often fail silently - giving confident-sounding answers that are wrong, and it can be hard to figure out what caused the failure. "Building and Evaluating Data Agents" is a new short course created with Snowflake and taught by Anupam Datta and Josh Reini. This course teaches you to build data agents with comprehensive evaluation built in. Skills you'll gain: - Build reliable LLM data agents using the Goal-Plan-Action framework and runtime evaluations that catch failures mid-execution - Use OpenTelemetry tracing and evaluation infrastructure to diagnose exactly where agents fail and systematically improve performance - Orchestrate multi-step workflows across web search, SQL, and document retrieval in LangGraph-based agents The result: visibility into every step of your agent's reasoning, so if something breaks, you have a systematic approach to fix it. Sign up to get started: https://lnkd.in/gVwKuCCQ

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We tested Snowflake's Gen2 warehouses on 2 billion query profiles across 14K warehouses here at Capital One. Here is where we saw improvement: 🍎 Approximately 30% of queries fell into categories that could benefit from Gen2—specifically those involving DELETE, UPDATE or MERGE. 🍎 Long-running DML operations: A significant subset of queries running longer than 30 minutes were performing complex DML tasks. These are prime candidates for Gen2 migration. 🍎 Large scans: Numerous queries scanned over 1 TB of data, where Gen 2’s improved scan performance can provide material runtime reduction. 🍎 Combined heavy patterns: A smaller but critical set of queries both scanned over 1 TB and ran for 30 minutes or more, often involving DML operations—these represent the highest potential gain from Gen2. These findings support a targeted migration strategy: focus on warehouses with a significant concentration of eligible workloads rather than migrating all warehouses indiscriminately. Check out the full blow written by our resident expert and all around awesome guy - Ganesh Bharathan https://lnkd.in/g8XufZaV

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Death by a thousand defaults on #Databricks. Databricks clusters: latest ML runtime + auto-termination set to 3 days. Dashboards: refresh every few minutes. Alerts: happily running queries on your most expensive cluster. Online tables: syncing more often than anyone needs. Not obvious. Sometimes hidden. Always expensive.

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I still see many teams comparing AI tools by features, then re-architecting six weeks later. So I mapped 12 real enterprise scenarios across LangGraph, LangChain, n8n, and AutoGen to make the choice obvious. Easy to understand example: Example: New employee onboarding (Day 0 → Day 1) Goal: Get laptop + accounts + access live in 24 hours, with approvals and audit. LangGraph: Model as a clear flow: HR webhook → verify docs → create checklist → request laptop → pause for manager approval (licenses/cost) → provision apps → confirm → if any step fails, resume/rewind from checkpoints (“time travel”) and retry. Great for guardrails and resumable steps. LangChain: Use as the LLM brain to read offer/role and generate: app list, access scopes, welcome pack, FAQs. Pair with another system to actually provision. n8n: Best for the glue: receive HRIS event → create Okta/Google Workspace users → open IT ticket → send Slack “Welcome” → calendar invites → approvals via approval patterns (Slack send-and-wait, forms, webhooks) → log everything to a sheet/DB. Low-code, fast. AutoGen: Planner + Tool-User agents decide the sequence and call APIs; add a supervisor to keep them on track. Useful if onboarding varies a lot by role—add strict stop conditions before any real changes. Routing rules Governed, recoverable steps → LangGraph Content/logic generation (who needs what, why) → LangChain Integrations, webhooks, approvals → n8n Flexible agent planning (lots of variations) → AutoGen Please share your experiences too . #AI #AgenticAI #LangGraph #LangChain #n8n #AutoGen #RAG #LLMOps #AIOps #EnterpriseAI P.S. All views are personal

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✨ Big news: the 2nd Edition of Data Modeling with Snowflake is officially out today! It's hard to believe that only two years have passed since the first edition, yet so much has changed. New Snowflake features, such as Dynamic and Iceberg tables, have transformed the way data is shared and processed. Additionally, the introduction of Cortex and the semantic layer allows AI to execute queries using natural language, adding even more capabilities. As AI continues to evolve, it's evident that data modeling is playing an increasingly important role in creating scalable and cost-effective data architectures. The rising costs of inefficiency highlight this relevance. However, despite its importance, data modeling remains far from ubiquitous. One major reason data modeling initiatives often fail is not due to technical issues, but rather personal ones: the engineering team often struggles to effectively communicate the business value to stakeholders. My favorite chapter in the second edition addresses this by breaking down business metrics and the ROI of data modeling. It provides data teams with a playbook for engaging stakeholders, persuading the budget committee, and fostering business alignment. Since publishing the first edition, I've heard directly from readers who have used this book to transform their careers and convince colleagues that data modeling is a worthwhile endeavor. I am confident that the second edition will build on that success and provide readers with even more tools to establish a solid foundation for creating efficient and scalable data solutions. A huge thank you to Joe Reis for writing the introduction to this edition and for promoting data modeling within the data community through his ongoing work. If you aren't already following him, be sure to do so, and keep an eye out for his highly anticipated data modeling publication. Also, stay tuned for a release webinar and AMA session in the coming week. I'll be taking questions and giving away a copy of the book as well. Get your copy on Amazon: https://a.co/d/iVak9qF or direct from the publisher: https://lnkd.in/dEjk-Xf3

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The announcement of the Open Semantic Interchange (OSI) initiative (https://lnkd.in/eG2kyU8k), led by industry leaders including our partner Snowflake, along with Salesforce, BlackRock, and 15+ other companies, validates what we've been passionate about at Narrative for nearly a decade: solving data's interoperability challenge is essential for AI success. We're thrilled to see this collaborative approach to creating open standards for semantic data exchange. It's a testament to how critical this challenge has become. While the industry works together on these important standards, we're proud that our Rosetta Stone platform is already helping enterprises bridge the gap today: - Beyond Metadata - Execute normalized data directly through our Narrative Query Language (NQL), our SQL extension that makes standardized data operational, not just just descriptive - Instant Integration - Unifying data from dozens of sources in minutes, not weeks - AI-Powered Normalization - Using models from 3B to 70B parameters for intelligent data translation - Standards-Ready - Built to adapt to emerging frameworks like OSI The results speak for themselves: - 5x faster data integration - 60% reduction in data prep costs - 34% increase in conversion rates for retail clients This isn't about choosing between standards and solutions—it's about the entire ecosystem moving forward together. Standards provide the framework; intelligent tools make them work in practice. Congratulations to everyone involved in the OSI initiative! We look forward to supporting these efforts while continuing to help enterprises unlock their data's potential today. #DataStandardization #AI #DataIntegration #OpenStandards #Innovation #DataManagement #EnterpriseTech #DataCollaboration #RosettaStone

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