Introduction

TL;DR: Snowflake continues on a path to produce a wider breadth of data products at a faster pace. At the same time, we see hints of a maturing market and ecosystem consolidation. 

Snowflake’s 2025 keynote didn’t drop features — it dropped signals. For anyone involved in scaling modern data stacks, this year’s announcements represent a decisive shift. Instead of shiny point releases, we’re now seeing Snowflake evolve into a data operating system — one that’s governed, open, AI-native, and increasingly programmable. The direction is clear: less glue code, more leverage.

What follows is a breakdown of what was announced, framed not by product hype but by the issues customers actually face. Whether you're migrating off legacy, struggling with cost governance, or hitting limits on orchestration and transformation, Snowflake is laying out answers.

We’ve organized this analysis into core themes — platform evolution, transformation, AI, interoperability, and cost — each one mapped to the outcomes that matter in practice: speed, governance, predictability, and operational clarity. This is not just a roadmap from Snowflake. It’s a checklist for rethinking how your architecture should evolve.

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Market trends & response.

Spend management still holds back modern data platform adoption.

As businesses ingest more data, run heavier workloads, and serve multiple departments from one platform, they hit the same walls: unpredictable spend, flatlining performance, and friction moving data between tools. Platform teams end up managing complexity rather than shipping value.

This year’s Snowflake announcements strike directly at that pain.

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Rightsizing takes the thinking out of spend control.

Snowflake is introducing a new compute paradigm that adapts to changing workloads — elastically, intelligently, and without the need for manual scaling. Today’s compute model is based on t-shirt-sized assumptions. This model will be unified to a paradigm that will rightsize the compute for the workload at hand, without engineers making assumptions. This isn’t just autoscaling; it’s a rethinking of how compute allocation flexes with real-world demand. That’s critical for bursty data loads, interactive analytics, and unpredictable AI workloads. 

As a result, Fewer performance cliffs, less overprovisioning, and better SLA alignment for both batch and real-time users.

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Making data accessible to Snowflake becomes even cheaper.

Ingest pricing has historically been a dark art. With this release, Snowflake is introducing a simpler, more predictable pricing model for high-throughput pipelines. While details are still being rolled out, the takeaway is clear: budgeting for streaming data just got less ambiguous.

According to Kleinermann’s keynote, this is designed to cater to the needs of customers who allocate a significant portion of their data platform budget to data ingestion. We can acknowledge that, as we have audited and solved such challenges over and over in the past.

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PostgreSQL workloads are first-class citizens now.

This is big. Snowflake is shipping a native Postgres engine, through the acquisition of CrunchyData, giving teams the ability to run Postgres-based workloads inside Snowflake without translation layers. For teams operating SaaS platforms, event stores, or microservice analytics pipelines in Postgres, this lowers the migration barrier dramatically.  Further, it’ll reduce latency in hybrid applications and all of our data workloads will be covered under the same contract terms.

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Migration & Transformation Modernized

Accelerators unblock everything that comes after platform migration

The value of Snowflake isn’t in migrating over a few tables — it’s in replatforming your decision logic. But for most organizations, the real blockers are upstream: legacy stored procedures, brittle ETL orchestrators, and tribal knowledge encoded in codebases no one wants to touch. Snowflake’s 2025 announcements recognize that transformation is the real workload, and they’re finally meeting teams where they are.

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Step 1 : Moving your legacy data transformations to Snowflake

Anyone who's tried to refactor legacy SQL or stored procedures knows the pain: untyped logic, deeply nested conditions, procedural weirdness from Teradata or Oracle. SnowConvert AI tackles this with an LLM-powered code translation engine that understands both syntax and context. It doesn’t just rewrite SQL — it interprets and converts logic, documentation, and behavior into Snowflake-native patterns.

Our own data platform migration acceleration goes even further by directly covering legacy technologies. 

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Step 2 : Orchestrating data workflows via OpenFlow (Managed Apache NiFi)

This might be one of the most strategic additions to Snowflake’s ecosystem: a managed orchestration layer built on Apache NiFi, branded as OpenFlow, through the recent acquisition of Datavolo. Instead of pushing everyone to Airflow or a third-party orchestrator, Snowflake is offering a stateful, declarative, low-code way to build and run data movement flows, with built-in observability and security.

As a result, you can replicate legacy ETL orchestrators with fewer moving parts. OpenFlow becomes the bridge between ingestion, transformation, and enrichment — all within your data perimeter.

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Step 3:  Snowflake now natively runs dbt workloads. 

Snowflake now supports dbt natively — not just in spirit, but in practice. Workspaces bring Git-based version control, file-aware editing, and native scheduling to dbt workflows directly inside the Snowflake UI. This aligns with how modern analytics engineering works: declarative logic, governed promotion, and CI/CD for transformation code.

Therefore, your transformation logic is now natively governed, easily auditable, and CI-friendly. It becomes easier to onboard, easier to secure, and far easier to scale.

Operational AI Comes of Age

AI has lived outside the warehouse for too long, and that’s held adoption back

The reality of AI adoption in data teams is this: models live in notebooks, pipelines run in separate tools, and results land back in the warehouse days (or weeks) later. Governance suffers, speed dies, and reproducibility becomes an afterthought. Snowflake’s 2025 announcements signal a turning point — where AI becomes native to the platform, operational by design, and usable by analysts.

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An faster way to combine analysis across structured and unstructured data

Snowflake introduced Cortex AI SQL — a set of functions that embed LLM power directly into your SQL workflows. This isn’t AI as a service — it’s AI as a first-class SQL construct. Whether you're summarizing customer feedback, classifying support tickets, or embedding documents for retrieval, you do it inside a governed, scalable environment.

What did we like about this? No extra stack. No handoffs. Just immediate AI value where your data already lives.

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Unstructured data sets are now first class citizens, too.

With native support for the FILE data type, Snowflake now treats unstructured data — like PDFs, images, and audio files — as first-class citizens. This bridges a longstanding gap where semi-structured and blob data required roundabout processing.

You can transcribe, embed, and analyze multimedia data directly, without staging it elsewhere or writing custom handlers. Less engineering, faster time-to-value.

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Data science, without the data scientist.

Whether you're doing customer ticket summarization, product feedback classification, or fraud pattern embedding — Snowflake now lets you operationalize it. In the keynote demo, AI_AGG() and AI_FILTER() were used together to extract and group insights across complex datasets. The cognitive load was handled by the AI, but the logic and control stayed in SQL.

This isn’t AI for AI’s sake. This is AI as a lever — built for operations, embedded in governance, and accessible to the people already building with data. Snowflake just made your warehouse not just a store of data, but a platform for intelligence.

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Interoperability & Governance at Scale

Data sprawl leads to blind spots, duplication, and compliance risk

As companies scale, data multiplies across clouds, teams, and tools. Without a shared governance layer, lineage breaks down, sensitive data hides in plain sight, and metadata becomes an afterthought. Worse, interoperability usually comes at the cost of governance — or vice versa. Snowflake’s 2025 updates aim to end that tradeoff.

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At last, a 360° degree across all workflows and platform components

Snowflake’s Horizon Catalog isn’t just a metadata store — it’s a governed discovery layer that spans Snowflake and beyond. It connects to external engines (Trino, Presto, Spark), open table formats (Iceberg, Parquet), and metadata services (Glue, Polaris, Gravitino). This makes your warehouse the anchor, not a silo.

Outcome: You gain unified visibility into your data estate, regardless of where the compute happens.

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A hint at ecosystem consolidation?

The platform now includes:

• Lineage Visualization: Native, navigable data lineage
• Auto Classification & Tagging: ML-driven detection of sensitive fields
• Access Reporting: Who accessed what, when, and how
• Synthetic Data Generation: Useful for testing and sandboxing use cases
• Model-Level RBAC: Apply permissions to AI/ML models directly
  
  

You get enterprise-grade governance without duct-taping third-party tools together. And it scales with you. This feels like we can slowly consider bypassing catalogs like OpenMetadata.

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About lock-ins & integration with competing vendors

Last year’s biggest market shakeup was without any doubt the Apache Iceberg momentum. So this part was quietly powerful: Snowflake now supports federation via Apache Polaris, allowing teams to connect external catalogs to Horizon, including Dremio, Gravitino, and Glue. Combined with Iceberg enhancements (positional deletes, unmanaged writes, variant data type support), this positions Snowflake as the governance and interoperability backbone for open data architectures.

If you’re building hybrid stacks — Databricks here, Postgres there, Parquet elsewhere — Snowflake can now govern them all. Without forcing a data movement decision.

Polaris, by the way, was announced last year at Snowflake Summit as the company's own effort at creating an open catalog format for Apache Iceberg. It was donated to the Apache Software Foundation later that year.

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This isn’t Snowflake doubling down on lock-in. It’s the opposite. By embracing open formats (Iceberg), open catalogs (Polaris), and open compute (Presto, Spark), Snowflake is inviting the rest of your architecture to participate, while still giving you the benefits of centralized control.

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Managing workload spend by default

Actually, the keynote actually kicked off with Finops messaging. Personally I have mixed feelings about that. We all see our Linkedin timelines spammed with competitive messaging about spend, whereas professionals who’re involved in operating data platforms on a day-to-day basis are well aware that spend is largely an architectural choice. 

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Without usage transparency, cloud data costs spiral and accountability erodes

As Snowflake becomes central to analytics, transformation, and now AI, the cost profile changes. What used to be a warehouse line item is now a full data platform expense. But while usage scales, visibility often doesn't. Finance teams can’t trace spend to outcomes, and engineering teams can’t self-manage budgets. The result? Fire drills, surprise invoices, and platform distrust.

Snowflake’s 2025 Cost Management updates are a direct response to this problem.

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We'll get alerts on anomalous spend

Snowflake now proactively flags abnormal usage spikes. Think of it like a safety net for runaway queries, broken pipelines, or AI workloads gone rogue. With anomaly alerts baked into the UI, teams can catch issues before they hit the billing cycle.

This is - in our opinion - a critical addition to change management and continuous improvement of your platform’s target operating model.

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Cost allocation at the data product level

Perhaps the most powerful FinOps feature: budgets tied to resource tags. By applying tags to warehouses, workloads, or data assets, you can allocate budgets at a granular level and monitor in near real-time.

Teams can own their spend. Finance gets clarity and cost allocation at increment level (!) . Engineering gets constraints that actually make sense.

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Closing Thoughts

Snowflake’s 2025 keynote wasn’t just a product update — it was a repositioning. What used to be the industry’s favorite cloud data warehouse is now becoming the data operating system for modern enterprises. One that connects ingestion to transformation, AI to governance, cost to accountability, and legacy to what's next.

The key themes this year were loud and clear:

• Unified transformation paths — with OpenFlow, SnowConvert AI, and dbt-native workspaces
• AI as a primitive, not a sidecar — composable, governed, and SQL-native
• Governance without lock-in — powered by Horizon, Iceberg, and Apache Polaris
• Operational clarity — through adaptive compute, spend visibility, and organizational usage insights
  
  

For data teams, the message is: your architecture just got simpler. You can now do more with fewer tools, fewer dependencies, and more control, without sacrificing interoperability or governance.

For leadership, the takeaway is this: Snowflake isn’t chasing categories — it’s defining the standard for what a data platform should be. If your current setup is fractured, fragile, or expensive to scale, now is the time to reassess. Not to chase trends, but to realign around outcomes: faster time-to-insight, fewer integration headaches, and predictable operational cost.

At Tropos, we’ve mapped these directions into repeatable migration paths, transformation governance patterns, and AI enablement playbooks. If you’re ready to shift from just using Snowflake to truly building on it — we’re ready to help you lead that shift, end-to-end.

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