We had a big problem at Max Burgers: our basic cloud container setup kept crashing under heavy load. We weren't delivering insights on time, and we needed something better.

We tried the big enterprise platforms. The setup was more complicated than we had hoped for, maintenance ate up all our time, and the learning curve was brutal. We were spending time fighting the platform that we'd rather use to work with our data.

Finally, we found Snowpark Container Services. After migrating, we now get the performance we need without the headache. We're spending less on infrastructure and our developers can focus on building instead of dealing with crashing containers.

I'll walk you through what we've learned. Which architectural choices worked, how we split workloads across clusters, and how we handled orchestration. If you're dealing with similar container headaches or trying to figure out your data ops strategy, perhaps our learnings can save you some time.

More information coming soon. 

More information coming soon

Join LTIMindtree and Scania to explore how the modern data stack—powered by cloud services, and a data product mindset—transforms raw data into governed, reusable, and business-aligned assets. By applying domain-driven design and Data Mesh principles, organizations can decentralize ownership, align data products with business needs, and foster scalable collaboration. This approach accelerates decision-making, strengthens data-driven strategies, and delivers measurable business outcomes

DNB, Norway’s largest bank, began building a cloud-based self-service Data & AI Platform in 2017, delivering its first capabilities by 2018. Initially focused on ML and analytics, the platform expanded in 2021 to include traditional data warehouses and modern data products. Snowflake was officially launched in 2023 after a successful PoC and pilot.

In this talk, we’ll walk through our journey.

Where We Came From

•Discover how legacy data warehouse bottlenecks sparked a shift toward decentralised, self-service data capabilities.

Where We Are

•Learn how DNB enabled teams to own and operate their data products through: •Streamlined domain onboarding •“DevOps for data” and “SQL as code” practices •Automated services for historisation (PSA)

Where We’re Going

•Explore how DNB is evolving its data mesh with: •A hybrid model of decentralised and centralised data products •Generative AI, metadata automation, and development support •Enhanced tooling and services for data consumers

Explore Snowflake’s enterprise AI vision, innovations, and roadmap. You’ll see how Snowflake Intelligence, Cortex AI SQL, Cortex Agents, automatic Semantic Model generation and specialized AI tools work together inside Snowflake’s secure platform, turning multimodal data into transformational impact for enterprise customers without infrastructure headaches. Walk away knowing how these integrated capabilities give business users, data analysts and engineers the tools, speed and governance required to deploy AI into production.

EQT, a global investment organization specializing in private capital, infrastructure, and real assets, has transformed its data operations by fully adopting the modern data stack. As a cloud-native company with hundreds of internal and external data sources — from YouTube to Google Cloud Storage — EQT needed a scalable, centralized solution to ingest and transform data for complex financial use cases. Their journey took them from fragmented, Excel-based workflows to a robust, integrated data pipeline powered by Fivetran.

In this session, you’ll learn how:

•EQT streamlined external data ingestion and broke down data silos •How a unified data pipeline supports scalable financial analytics and decision-making •Fivetran’s ease of use, connector maintenance, and cost-effectiveness made it the clear choice

Join Maria Wiss, Daniel Myers, and Angela Carpenter, as they kickoff Snowflake World Tour Stockholm, share vision for the AI Data Cloud, customer stories and make exciting product announcements.