Innovations at Domino’s with Snowflake – Bianca Gilchrist – Domino’s

Bianca Gilchrist, Domino’s Data Literacy & Visualisation Lead and Snowflake Data Hero of the Year, spoke about building a data literacy culture at Domino’s and driving the rollout of Snowflake CoWork, branded internally as Daria, to non-technical staff.

• “Every story, every report that we put out has to have a story behind it” – reports should read like an analyst’s narrative, not a grid of numbers
• Run weekly “15-minute bites” sessions where staff watch live Q&A with Daria on calls – showing how easy it is to ask a question and get an answer, which then prompts a flow of similar questions from the team over following days
• Wrote two data literacy books for children: “the A to Z of data“ and “The Australian Data Story“, and is developing a schools program with Snowflake to bring young kids into the data field
• Three pillars of data literacy: accessibility (can people find the data), usability (what can they do with it), and storytelling
• Marketing data internally – relentless, visible promotion (Viva posts, being put in front of every group) is essential to getting data trusted and used
• One of the biggest recurring business questions the agent gets used for: when a limited-time offer or new product launches, is it cannibalising sales from other products – this previously took a Business Analyst half a day to a week to answer in Power BI, now answered by Daria in minutes
• You need to show a working model quickly, since people can’t visualise an idea from a description alone
• Had to explicitly instruct Daria to be more pessimistic — it was originally overly optimistic (e.g. overstating cost savings), so instructions were added to correct this and avoid dollar-figure recommendations, and to only make recommendations in the voucher area, not financial matters
• Staff are given daily prompt limits partly to control cost, but also to keep people using the core “gold standard” Power BI reports for standard metrics, ensuring the numbers people see match what the CEO sees
• Cost and usage limits are a live tension, staff loved unlimited prompts for two months and pushed back hard on new daily caps
• Future focus is to get the AI agent to redirect simple, reportable questions back to the core Power BI reports, and expanding into specialised sub-agents

Power BI Report development with Snowflake CoCo – Brad de Bruyns – Vivanti

Brad de Bruyns, Senior Consultant at Vivanti (and a former Domino’s colleague of Bianca’s), demonstrated a code-first approach to Power BI development using Snowflake CoCo, showing how AI can turn report-building into a version-controlled, code-based workflow rather than manual clicking in binary files.

• The traditional Power BI PBIX format is opaque and can’t be diffed, whereas the newer PBIP format exposes reports as text so it is version-controllable, diffable, and peer-reviewable in Git
• Snowflake Semantic Views were used as the single source of truth for metrics, shared across both CoWork (formerly Snowflake Intelligence) and Power BI
• Demonstrated CoCo “Skills” baked directly into the repository – encoding branding, DAX standards, layouts, and complex JSON so AI-generated reports and measures come out brand-compliant and consistent every time
• Walked through a live OpenSpec demo: a plain-English request (“add a detailed credit consumption by type page”) was turned into a structured Propose > Explore > Apply process, generating a proposal, design doc, and task list before any changes were made
• Deployment ran through a disciplined Azure DevOps CI/CD pipeline, validating and deploying automatically across Dev, Test and Production
• Underlying agent is CoCo, essentially Claude-like – any Claude-style skills apply the same way
• The Power BI semantic model and the Snowflake semantic view are currently separate but kept in sync via the same underlying spec with a further skill able to uplift a Power BI model into a semantic view
• Manage multiple reports / workspaces in one repo (config-driven via subfolders and deployment targets)
• Started from screenshots / Figma mockups instead of specs and outputs remain fully manually editable afterwards, since it’s still a standard Power BI file underneath
• Less time memorising palettes and file structures, more time on the data modelling and insights that actually matter

Highlights & Key Takeaways from Snowflake Summit 2026 – Majid Miri & Yuqi Zheng – Snowflake

Majid Miri (Senior Solution Engineer) and Yuqi Zheng (Associate Solution Engineer) closed the evening with a rundown of Snowflake Summit 26 announcements, framed around the theme of the “agentic enterprise” and what’s needed to scale AI safely across an organisation.

• CoWork and CoCo rebrands: Snowflake Intelligence is now CoWork, the agent for business users; Cortex Code is now CoCo, the builder’s agent — both reaching a wider surface (desktop, mobile, Slack) since launch
• Horizon Context and Cortex Sense: supply governed business context and definitions to agents, aiming to eliminate the cold start problem of enterprise AI needing weeks of manual semantic modelling
• AI Agent Identity: gives every agent a verifiable, cryptographic identity and a complete audit trail, distinguishing agent-driven queries from human ones for access control purposes
• Apache Iceberg v3: reached general availability, with broad support for new data types, cross-system change tracking, and high performance on semi-structured data paired with Snowflake-managed Iceberg storage to cut operational overhead
• OpenFlow and Datastream: managed data ingestion, OpenFlow for extract/load workflows with new connectors, Datastream as a new Kafka-compatible managed streaming service with separated storage/compute scaling
• Control plane for a governed agentic enterprise – the connective layer of data, context, identity, and governance needed to run AI safely at scale
• Gartner predicts that by 2028 the average Fortune 500 company will have over 150,000 agents, underscoring the need for centralised governance and observability
• Deepened partnership with Anthropic, with Claude positioned as the default reasoning layer underneath both CoWork and CoCo

Source: The 2018 InfoQ Editors’ Recommended Reading List: Part Two

Source: ThoughtWorks Sold to Private Equity Firm Apax Partners

• Storage is so simple you don’t need to think about it – data lake architecture
• Simply select how much memory and how many cores and it computes the cost
• Google is quick because they are laying their own fibre and using their own infrastructure – less than a minute to spin up a VM, with no local data centre
• Chrome has a RDP client – who knew
• Google philosophy is you shouldn’t have to hire someone to manage the pricing
• Bioinformatics is new to the cloud and they are in great need of engineers, Google is having a stab at a genomics API
• BigQuery is NoOps, has been evolved to be a data warehouse, with no servers,
• Big Relational if you just want SQL, Amazon Aurora is the fastest growing cloud database, Google has released Cloud Spanner, first database to truly meet CAP theory
• BigQuery is column store, Cloud Spanner is OLTP
• MQTT seems to be coming the defacto standard for IoT
• IoT uses BigTable first and BigQuery second in Google reference architectures, because it is cheaper
• Python is the emerging ML language for some reason
• Teaching Kids Programming