Kenny Ro

AI agents are only as powerful as their memory. Redis has been named the #1 data storage choice for AI agents in 2025. The reason? Redis delivers what AI truly needs: - Real-time vector storage - Semantic caching - Long-term memory for LLMs At Bassirah, we see this as validation of why Redis is central to the next generation of AI-native data platforms powering everything from agentic RAG systems to enterprise decision engines. 🔗 Read more: https://lnkd.in/db47k6zG #AI #Redis #RAG #LLM #DataEngineering #AIAgents #ArtificialIntelligence

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Databricks set a new direction at the 2025 Data + AI Summit. The new mission is clear: build and deploy full-scale data and AI products on a unified, AI-native platform. Here’s what’s new: • Lakebase – Real-time workloads, fully integrated • Agent Bricks – Build, deploy, and manage AI agents • Lakeflow + Declarative Pipelines – From pipeline code to pipeline intent • Lakebridge – AI-powered onboarding, accelerated • Unity Catalog Metrics – Governed business metrics, centralized • AI/BI – Business intelligence with an AI-native interface • Databricks Apps – Serverless, data-native applications • Databricks One – Unified view of metrics, apps, and agents • Free Edition – No-cost access for small teams and prototyping We broke down what each launch means, and how they’re changing the way teams execute AI at scale. Get the details: https://hubs.li/Q03sMykq0 #DataAISummit #Databricks #Indicium #AIExecution #IndiMesh #ModernDataStack

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What does it take to succeed in data science? Strong coding and AI/ML knowledge are essential — but they’re just the beginning. Check out my talk at the next MLOps Denver meetup where I'll share five key lessons from over eight years in the field. From advocating for yourself to building cross-functional collaboration, this talk goes beyond the technical to explore what truly drives long-term success in a technical field. Hope to see you there! #DataScience #CareerGrowth #AI #MachineLearning #WomenInTech #ProfessionalDevelopment

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🤝 Gravity is now a Databricks Built on Validated Partner, recognizing Gravity's production use of Databricks SQL, Managed MLflow, and Delta Lake at enterprise scale. What does this mean for healthcare organizations? Less time wrangling data. More time building AI that actually moves the needle. 💡 Gravity brings pre-built clinical and payer data models, industry-standard ontologies, and proprietary ML models and now, with Databricks powering the ML and AI infrastructure, your data science teams can start at insight and agent development, not ground zero. From pipeline to deployment. From data to outcomes. That's the future we're building together. 🔗 Explore more about our partnership: https://lnkd.in/g-rGRMBp #HealthcareAI #AIInfrastructure #HealthTech Innovaccer

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Databricks is the leading platform for data and AI. As Databricks grew, they needed to automate access reviews to meet FedRAMP governance requirements. With Opal Security, UARs happen automatically, instead of depending on fragile scripts and spreadsheets. Databricks also leverages Opal’s API and Databricks Notebooks to track persona changes over time. Learn how Databricks secures access inside the perimeter for 8,000 employees with Opal → https://lnkd.in/gE8Gr4DM

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Cross Posting #Databricks CEO Just Dropped The Most Honest Advice About The Future Of AI The next wave of AI success is going to be completely dependent on structured data. Maybe that’s a no-brainer to you. Maybe I’m telling you that the next time you take a drink of water, that water will be wet. They don’t say things like all software developers will be replaced by AI (the deadline for which has since passed) or all tech employees should become baristas and bartenders (the dream is over, nerds) or don’t worry, we’ve hammered out all the unintended consequences for letting users create their own AI At the same time, AI is, theoretically anyway, offering executives and leaders actionable insights, options, and answers. AI Is Motion, Data Is Experience AI is fire, while data is fuel. https://lnkd.in/gqcRq6vY

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Claude, ChatGPT, Snowflake Intelligence, Cortex Analyst, Databricks Genie, Zenlytic, TextQL. Think about any AI agent you connect to your data. They all get questions from business users like: “What was the average number of daily active users in the last quarter?” These AI agent connects to Euno’s MCP server to retrieve metadata. That’s when Euno gives these agents the missing context they need to understand your data. ** Just a reminder that Euno is a metadata platform so we only access your metadata ** Euno runs a semantic search and looks for dashboards that use DAU (which is semantically equivalent to Daily Active Users). It filters for certified dashboards using the trust signals you’ve already set in Euno. In this example Euno finds two candidates: 1. DAU used in the User Engagement dashboards 2. DAU_NEW used in the Product Statistics dashboard Both dashboards are certified. But Euno also shows how often each has been used in the past 30 days, along with the owner or creator. With that information, the business user can confidently choose the right one and the AI agent can deliver the correct answer. The same applies to tools like Cursor. Let’s say you’re an engineer and ask: “Can you find me the Tableau dashboard for Customer Success?” Cursor will reach out to Euno’s AI assistant to get that BI context because Cursor is focused on dbt development and doesn’t have access to that layer. You can even go further and ask which dbt models power that dashboard and how those models are calculated. Euno feeds Cursor with that real-time context as you build. It’s not just for business users. It’s for developers too.

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New research from Databricks. It's about training enterprise search agents via RL. KARL introduces a multi-task RL approach where agents are trained across heterogeneous search behaviors, constraint-driven entity search, cross-document synthesis, and tabular reasoning. It generalizes substantially better than those optimized for any single benchmark. KARL is Pareto-optimal on both cost-quality and latency-quality trade-offs compared to Claude 4.6 and GPT 5.2. With sufficient test-time compute, it surpasses the strongest closed models while being more cost efficient.

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Data is in the air! With back-to-back Snowflake and Databricks conferences in SF, one thing that resonated is that data is paramount for AI. While everyone's talking about data use in AI innovation, securing data in the age of AI couldn't be more critical. This week, our CTO, Priyanka Tembey, dives deep into why runtime protection is essential for securing data in use within modern AI systems. Discover how Operant's AI Gatekeeper simplifies this complex challenge, making robust AI security achievable: https://lnkd.in/gYcbixSB #AIGatekeeper #SecureAI #AgenticAI #SnowflakeSummit #DataAISummit #DatabricksSummit #AIPrivacy #DataInMotion #Runtime #DataCloud #AIReady #DataTransformation #RealTimeAI #Datasecurity

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We’re building powerful, real-world AI systems at CVS Health. Here’s a quick example from Lumina, the internal AI platform our team built. Documentation drift is one of the biggest problems in engineering teams. In this demo, we automatically turn a GitHub pull request into fully formatted documentation and publish it directly. No manual write-ups, no forgotten documentation, just engineering workflows that update themselves with zero effort for the engineers. Even better: this isn’t a proof of concept. It’s live in production. Over the past two years, CVS Health has dramatically expanded our AI capabilities, and we’re now building systems that meaningfully improve how engineering teams operate at scale. Kudos to our talented engineering team, Parker Ljung, H Scott Petersen, Arpita Lonakadi, Gopi Krishna Gorle . Excited about where this is going. #AI #GenAI #DeveloperExperience #EnterpriseAI #CVSHealth

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Snowflake makes merge easy - MERGE ALL BY NAME When the target table and source must have the same number of columns and the same names for all of the columns, you can simplify MERGE operations by using MERGE ALL BY NAME. MERGE statements can update each column in the target table with the values of the column with the same name from the source table. MERGE statements can also insert rows from the source table into the target table based on column names when there is no match. These MERGE ALL BY NAME operations are supported even when the column order is different in the target and source tables. Example - Traditional way - MERGE INTO CUSTOMER_DIM AS tgt USING CUSTOMER_STG AS src ON tgt.NAME = src.NAME WHEN MATCHED THEN UPDATE SET tgt.CITY = src.CITY, tgt.COUNTRY = src.COUNTRY WHEN NOT MATCHED THEN INSERT (NAME, CITY, COUNTRY) VALUES (src.NAME, src.CITY, src.COUNTRY); New way - MERGE INTO CUSTOMER_DIM AS tgt USING CUSTOMER_STG AS src ON tgt.NAME = src.NAME WHEN MATCHED THEN UPDATE ALL BY NAME WHEN NOT MATCHED THEN INSERT ALL BY NAME; Note - if you merge contains and transformations avoid using this. Official snowflake document - https://lnkd.in/g4R3c_Rk Snowflake Snowflake Developers

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If you haven't tried DQX for data quality checks, you should! First released almost a year ago, it quickly became a default for Databricks users. You can validate your data using json, yaml, or Python, and quarantine data that does not pass the checks. Below an example of an yaml file. Check out the latest article on Cauchy's Substack by Steven Kempers on how you can use DQX: https://lnkd.in/ecTraxxX. It is a part of our 6-part series on data engineering. Subscribe not to miss the follow-up!

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