Tim Spann

We’re seeing a massive shift in delivery velocity using Snowflake Cortex Code (CoCo). Because it’s native to Snowflake, it actually understands your schema, metadata, and architectural patterns. A few high-level takeaways from Heather Rhyne-Christensen's blog post on the subject: ✅ dbt development: Scaffolding models in minutes, not days ✅ Legacy migration: Converting complex ETL (like Talend) to Snowflake SQL with 80% automation ✅ Self-healing pipelines: AI-assisted debugging that identifies root causes in real-time It’s not just about writing code faster; it’s about redefining how services are delivered. 🚀 Learn more here: https://lnkd.in/eiJ_DbRa

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Great news for Spanner and BigQuery users. Datastream now provides a seamless, serverless bridge from Spanner’s transactional power to the high-performance analytical capabilities of BigQuery and BigLake. Get the most scalable and available database that runs Google with BigQuery and BigLake’s serverless AI-Native Lakehouse!

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We've launched the dbt_semantic_view package, seamlessly integrating the Snowflake semantic view with dbt's disciplined workflow. This means you can now: - Centralize Metrics: Define key metric logic (FACTS, DIMENSIONS, METRICS) natively in Snowflake's semantic view for a single source of truth. - Enable CI/CD: Apply dbt's strengths (declarative YAML, testing, and version control) directly to your semantic layer definitions. - Ensure Governance: Automatically manage privileges and validate relationships to ensure every consumer, from BI tools to AI agents, uses trusted data. - Unify your governance framework and ensure consistent, high-performance metrics across your enterprise. Learn more and get started: https://lnkd.in/eZqiwT-g

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Just published a new research note with my colleague Adam Ronthal on the future of the current AI-native/vector-only database market and what those vendors and traditional DBMS vendors will need to do to compete. Here's the link for those with a subscription: https://lnkd.in/e4qsXWHR.

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Why should Active Metadata Tags be the backbone of your AI strategy? Active Metadata Tags (AMTs) are used for automatic enrichment of metadata. Euno supports two types of custom metadata enrichment capabilities, both of which generate AMTs: #1 Extract structured metadata from unstructured metadata. This involved parsing metadata from elements like code and descriptions. A common application would be to enforce data modeling guardrails. Examples: → Implicit columns schema: Analyze dbt model code to determine whether the resulting schema includes implicit or explicit columns. → Verification checks: Assess naming conventions, detect use of approximate count distinct when querying large tables, evaluate documentation agility, flag full outer joins, and more. #2 Compose new AMTs. This approach combines metadata of a resource, and of any related resources, to set a new property on a resource. Examples: → Non-trivial custom SQL: Tag Tableau Custom SQL nodes that do not add logic. → Certified data source: Tag Tableau data sources as ‘certified’ if they don’t contain non-trivial custom SQL. → Certified dashboard: Tag Tableau dashboards as ‘certified’ if they only depend on certified data sources.   This capability is essential for governance workflows in large organizations where different teams maintain different parts of the stack. So team AI can build on something that team B created. Consider this scenario: → The team maintaining dbt can create a custom property to flag models for deprecation (e.g., models lacking both ‘prod’ and ‘prod_ready’ tags). → Analytics teams can then use this signal to create custom properties for their BI assets to drive workflows to migrate away from those models. Why does this also matter for AI agents? By implementing AMTs to surface trust signals, AI agents can actually tell the difference between exploratory work and official definitions. Example: → Euno’s MCP server surfaces to AI agents only Tableau logic that meets your trust standard and is considered certified such as: ‘depends only on dbt models from the gold layer and was in use in the past 60 days.’ *** Expect to hear much more about Active Metadata Tags! They’re quickly becoming the key to scale AI beyond the sandbox in your organization. #ActiveMetadata

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Quartr API datasets are now available via Snowflake. Access structured earnings data – live audio, real-time and historical transcripts, filings, slide decks, and event summaries. Pull Quartr data directly into your existing Snowflake workflows and augment your datasets. No new pipelines, no separate API integration. Build with data that drives decisions.

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Did someone say large context window.. How about 10 million token on Snowflake #CortexAI? Snowflake integrates Meta's Llama 4 large language models (LLMs), Maverick and Scout, into its Cortex AI platform. • Llama 4 models utilize a Mixture of Experts (MoE) architecture for efficient and high-quality inference. • Llama 4 Maverick excels in image and text understanding across 12 languages, while Llama 4 Scout boasts a 10-million-token context window ideal for complex tasks. • Access is provided via SQL functions and REST APIs, enabling seamless integration into existing applications. • This collaboration empowers Snowflake customers to build advanced AI applications leveraging the power of Llama 4 within a secure and governed environment. https://lnkd.in/gJr3j7ms

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Your subgraph or Connector in a federated #GraphQL architecture doesn’t exist in isolation. It’s part of a larger system where client queries span multiple services, and performance bottlenecks can cascade across the entire graph. Now available, Subgraph and Connector-level insights in GraphOS Studio. Enable your teams to quickly understand your services’ behavior in the context of a larger federated graph, helping you identify issues and proactively improve performance. Learn more about how to get started: https://lnkd.in/e2b-RC2p

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Most data stacks weren’t built for AI. They’re siloed, hard to govern, and can’t scale beyond POCs. ▶️ Watch now and learn how an AI-ready stack — built on Snowflake — changes that. Let’s build smarter. https://lnkd.in/dKzJpDZp #AIReady #DataArchitecture #SnowflakePartner #BlueCloud

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Scalestack uses AI to automate company research for sales teams at leading tech companies like Databricks and Ramp, helping them spend more time closing deals instead of digging for data. With Redis Streams as the nervous system of their AI-powered GTM orchestration engine, Scalestack gained enterprise-scale performance, faster sales intelligence, operational excellence, and simpler sales tech. Swipe for a full breakdown. Read more: https://lnkd.in/gr4tyTHS

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What does it say about the Databricks and Snowflake engines that to provide fast enough query for AI that they need to turn to Postgres? Here's what is going on. Scanning engines (index free approaches) can't provide hot query at scale, because they have to scan too much unnecessary data. For example, in Google BigQuery's white paper they talk about how it would require 10,000 disk drives and 5,000 processors to scan 1 TB of data in 1 second. Even for Google, hard to split the data into enough pieces to get that sort of parallelization - leaving you with slow query times. Index free scanning engines exist in the first place because relational engines like Postgres can't index the data fast enough because of concurrency problems in their data structure (e.g. BTrees). Therefore, to scale you have to shard across lots of Postgres instances. This is very expensive. In my opinion, this is a capitulation by Databricks and Snowflake as their customers look at other solutions such as another Postgres variant in AWS Aurora for faster query for AI. Is there any other explanation? Transactional use cases? If so, that just highlights you can't get that with index free engines. So what do you get with them? I'm afraid the answer may be very convenient infrastructure to generate monthly reports. Everything in data revolves around the indexing problem. Craxel's algorithmic innovation, an O(1) indexing solution that works for complex data, means you can organize data at line speed with transactional consistency so that you can have hot query at petabyte scale without the cost of these older technologies. Finally, with Craxel's Black Forest, you get instant access to fully connected data (knowledge graphs) at massive scale. Anyone want to try to build connected data (e.g. graph) with Databricks, Snowflake, Aurora, or Postgres? The headline is wrong - Postgres hasn't won. Craxel's customers are the winners. https://lnkd.in/eeFpPfQA

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If you’re using Snowflake’s #semanticviews, this is worth your time. AtScale just released a free, #opensource CLI tool that converts semantic models written in SML directly into Cortex-ready semantic views & YAML. ✅ Open-Source SML → Cortex semantic views & YAML ✅ Open-source and free ✅ Extend your semantic model beyond #CortexAnalyst to work with #Excel, #PowerBI, #Tableau, #Looker and more. We've seen that ~80% of AI project failures stem from inconsistent definitions and poor data governance. Natural language tools like Cortex Analyst are only as good as the semantic definitions behind them. These new tools make it easier to plug trusted business logic directly into Snowflake’s AI workflows, without rebuilding from scratch. Explore the repo and let us know what you think. 🔗 https://lnkd.in/eNcUFa2P

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Our paper on Optimizing Compute Resource Commitments for Multi-Cloud Workloads was accepted to ICPE 2025! We characterized three years of virtual machine demand across Snowflake, including the transitions to new hardware generations, software changes, pricing for different long-term commitment levels, performance and VM provisioning latency, and formulated a series of optimization problems to help maximize availability and minimize cost. I am especially excited that we were able to release an anonymized subset of this data to support further research into cloud forecasting, commitment optimizations, and capacity planning. Read on for a short blog about the ideas and a link to the full paper and dataset.

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