David M Williams | Published 3 June 2026
While the business world is distracted by the promise of conversational AI, the real story at Snowflake Summit 2026 is happening in the engine room. Snowflake is completely rewiring its infrastructure to handle the massive compute, interoperability, and streaming demands required to actually make autonomous AI work at scale.
For data engineers, developers, and security architects, Snowflake made significant announcements during this week's Snowflake Summit 2026, shifting the tectonic plates of the AI Data Cloud.
We're definitely in the Iceberg era and with AI, to be reliable, requiring universal governance. Snowflake is aggressively moving to tear down data silos by heavily backing open standards. The company announced the general availability of Apache Iceberg v3, boasting the broadest feature support on the market for the open table format.
Further, Snowflake is a major contributor to the next, v4, spec. Snowflake VP of product, data engineering Chris Child, explained to iTWire that Snowflake is adding more and more into Iceberg to give it many of the great features and capabilities available in Snowflake native tables; being an open standard, this means those enhancements become available to implement by all other Iceberg-compliant systems too. Child said while that may seem to benefit competitors, it's ultimately to ensure the best outcome for the consumer. It also means Snowflake has to continue to focus on being the best and greatest query engine.
However, the biggest leap in interoperability comes from the Snowflake Horizon Catalog, which is now powered by Apache Polaris. This integration enables bi-directional read and write access to Snowflake-managed Iceberg tables from external engines using open, standards-based access controls. Snowflake is also supporting the Iceberg REST Scan Plan API, ensuring that fine-grained governance protections – like row access enforcement and column masking – apply consistently across compatible third-party engines. The end goal is a fully interoperable lakehouse where enterprises maintain a single, governed, live copy of their data across clouds and tools, without the cost or risk of duplication.
Additionally, while Apache Kafka brings a lot of power in data streaming, it can be well complex. Managing Kafka clusters to get streaming data into Snowflake has traditionally been a painful, complex, and expensive chore for data engineering teams. Snowflake’s answer is Datastream, a fully managed, Kafka-compatible streaming service built natively into the platform.
Datastream is a drop-in replacement that connects to existing Kafka ecosystems with zero code changes. Architecturally, it operates quite differently from traditional Kafka. The processors in Datastream are stateless; instead of local memory, they write directly to blob storage (S3 in AWS, for example), acting as the stateful store alongside a highly available, in-memory metadata store.
Because Datastream writes events to disk in a format Snowflake controls, a topic in Datastream is treated exactly like a table in Snowflake. This brilliant architectural bypass means engineers can skip the ingest step entirely, dropping latency to sub-second levels while significantly cutting the total cost of ownership by eliminating the need to manage complex clusters of brokers.
When it comes to training, Snowflake is bringing GPUs to the data with Cortex Training. Moving enterprise data to train AI models is expensive and fraught with security risks. Snowflake's solution is Cortex Training, a new capability that gives enterprises access to fully managed GPUs to customise and train open-weight foundation models (like the Qwen or Mistral families) directly where their data lives.
This allows engineering teams to use techniques like reinforcement learning to build highly specific, domain-aware models that outperform general-purpose APIs on reasoning tasks, without having to stitch together infrastructure across multiple platforms or move sensitive data. According to Snowflake, this allows teams to complete up to two times more training runs for the same GPU budget.
Snowflake CoCo (formerly Cortex Code) is receiving major upgrades specifically for builders. It is moving beyond the browser, launching as CoCo Desktop with extensions for VS Code, Claude Code, and Microsoft Excel.
More importantly, CoCo can now execute work autonomously. Cloud Agents allow developers to start a task and have it run securely in the background without needing to keep a local session open. To ensure these agents don't wreak havoc on sensitive systems, Snowflake introduced a secured local sandbox that isolates the agent's environment to protect files and system resources.
When it comes to agents, some have likened agentic AI to giving a person your credit card and asking them to buy a hat, and they come back having purchased a car. In comes zero-trust security for autonomous agents. As AI transitions from answering questions to executing tasks, traditional human-centric access controls are no longer sufficient. Snowflake is addressing this with Agent Identity, a security model built for autonomous actors. It gives agents a verified identity before they can access systems, enforcing strict, role-based permissions and maintaining a complete audit trail.
To defend against rogue agents or insider threats, Snowflake is also introducing multi-party approvals. For highly sensitive operations, the system will now require two or more administrators to sign off before an action is executed, ensuring autonomous AI does not become a liability.
One of Snowflake's greatest competitive distinctions has been its separation of compute and storage, which was previously unheard of until the Snowflake creators changed cloud and data economics with the warehouse model. Yet, warehouse tuning – while helping you manage your costs by specifying different resources for different workloads – has been somewhat of a science for many users. Snowflake's warehouse system used T-shirt sizing (XS, S, M, L, XL, and so on) which were easily-understood multiples of credits. Next-gen warehouses then came out providing more power, albeit at a "credit and a bit" fractional pricing. Now, Snowflake is potentially bringing an end to pre-provisioned warehouses, with its new Adaptive Compute coming out in general availability, to power this new era.
This feature aims to eliminate the classic Snowflake "t-shirt sizing" of warehouses. Instead of manually provisioning compute, the platform automatically determines and procures the optimal amount of resources required for each specific query in real time. Early benchmarks show this architecture delivering 1.6x faster analytics, 2.2x more throughput, and 3.5x faster DML operations, creating a true serverless experience that stops companies from wasting money on oversized warehouses, and driving even further to a fully-managed, seamless Snowflake experience which "just works".
3 June 2026
3 June 2026
2 June 2026
2 June 2026
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