Every year, the Semantic Layer Summit gets a little bigger, bolder, and much more urgent. And 2025 was no exception. As enterprises push the limits of AI, demand trustworthy analytics, and struggle with ballooning data complexity, the industry is waking up to what we at AtScale have long believed: the semantic layer is the key to unlocking real enterprise intelligence.
This year’s summit brought together data leaders, technologists, and innovators to chart the future of business intelligence and generative AI. I had the privilege of kicking it all off with our keynote, which looked at the massive trends shaping the modern data stack and the innovations we’re delivering at AtScale to help customers thrive.
In case you missed it (or want a recap), here are the big takeaways from the 2025 Semantic Layer Summit—and why this moment matters more than ever.
We’ve Reached the Inflection Point for Semantics in the Enterprise
It’s been amazing to see the momentum build around semantic technology in just the past year. From Snowflake and Databricks to Walmart, every major player is talking about their semantic layer. But what’s driving the urgency?
Simply put: GenAI has changed everything.
Large language models have ignited excitement—but also fear. And with good reason. Without a shared business context, LLMs can’t be trusted to produce consistent, correct, or secure answers. We tested this firsthand using the TPC-DS retail benchmark. Left to its own devices, an LLM was wrong 80% of the time. But grounded in a semantic layer? It achieved near-perfect accuracy.
That’s why the semantic layer is no longer optional. It’s foundational. It gives GenAI—and every analytics tool—access to governed, contextualized, and business-aligned data.
A Semantic Layer Strategy Is Also a Cost-Savings Strategy
Cloud data platforms have made analytics more accessible—but they’ve also turned compute into a blank check. Untuned, redundant queries are burning through OPEX budgets, fast. This is where the semantic layer quietly becomes your cost-control engine. With well-optimized query plans and intelligent aggregates, AtScale ensures your workloads stay lean, even as demand grows.
And now, with In-Memory Aggregates, we’ve taken that performance to the next level—automatically caching high-demand data in RAM for sub-second response times, even on large, complex datasets.
Composable Analytics: The End of Top-Down BI
For years, analytics was built top-down: centralized teams, rigid pipelines, slow feedback loops. That model no longer fits today’s pace of business.
Composable analytics is a better path forward. It’s decentralized by design—empowering domain teams to build, extend, and reuse semantic models aligned to their unique needs. But it also ensures consistency and governance through shared standards and inheritance.
With our new Composite Modeling capability, teams can now extend trusted semantic models instead of starting from scratch. This enables cross-functional metrics—like combining revenue from sales with cost from finance to model profitability—in a consistent, scalable way. Composable analytics is how we democratize insights without chaos, and the semantic layer makes it possible.
Open-Source Semantics Are Here: Introducing SML
With the explosion of semantic layer vendors, the risk of fragmentation is real. That’s why I’m incredibly proud of our most important contribution yet: SML (Semantic Modeling Language).
SML is the first open, multidimensional, and enterprise-ready modeling language for semantic layers—designed to unify the way we describe metrics, hierarchies, dimensions, and calculations across platforms.
Built in YAML, SML is human-readable, CI/CD friendly, and open-sourced under an Apache license. But we didn’t stop there.
We also launched:
- A public GitHub repository with pre-built models for TPC-DS, Salesforce, GA4, and more
- A full SML SDK for building and modifying models programmatically
- A CLI toolkit for validation, deployment, and interoperability
- Semantic Translators that bridge SML with formats from dbt, Power BI, Snowflake, and Databricks
SML isn’t just a modeling language. It’s a foundation for a shared semantic future. And I invite every data practitioner, vendor, and team to contribute. Explore SML on GitHub.
Real-World Impact: Customer Use Cases
Throughout the day, we heard from some of the most forward-thinking organizations applying semantic layers in production—transforming complexity into clarity, and turning data investments into strategic advantage.
- TELUS showed how they scaled telecom analytics by abstracting vendor-specific complexity in their radio access networks. Using the Semantic Modeling Language (SML), they automated KPI modeling and established a shared semantic foundation for technical and business teams alike.
“SML helped us remove the translation layer between engineering and the business,” said Adam Walker, Senior Design Specialist at TELUS. “Now we’re aligned on the logic and can scale insights across the organization.”
- Vodafone Portugal, with implementation partner Celfocus, walked us through their migration from a legacy OLAP stack to a cloud-native semantic layer on Google Cloud. They preserved familiar BI workflows while unlocking better governance and performance.
“We were able to modernize without disruption,” said Pedro Simão, Tech Lead at Celfocus. “AtScale gave us a bridge to the future—without breaking what worked.”
- Blue Mercury, HSBC, and Home Depot shared best practices for successful semantic layer rollouts in large enterprises. From unifying KPIs to driving adoption across teams, these leaders emphasized the importance of alignment, planning, and transparency.
“Enter AtScale—you provided the right type of on-prem and cloud technology that we were looking for. It integrates directly with BigQuery and really helps us accelerate our delivery speed and reduce the development complexity.”
– Rick Ramaker, Senior Director of Technology Data & Analytics, The Home Depot
Whether the goal was cost reduction, analytics scale, or AI-readiness, every story came back to the same core value: governed, reusable business logic that works across people, tools, and technologies.
Looking Ahead: A More Accessible, Open, and Certified Future
We’re not done yet. As we look toward 2026, here’s how we’re expanding the AtScale ecosystem:
AtScale Free Developer Edition
Anyone can now get started with semantic modeling—for free.
AtScale Snowflake Native App
You can now deploy AtScale directly inside your Snowflake instance via Snowflake Marketplace—no infrastructure setup required.
Analytics Tool Certification Program
Vendors can now certify that their products integrate cleanly with the AtScale semantic layer—ensuring seamless experiences for joint customers.
Final Thoughts: The Future of AI and BI Is Semantic
If there’s one message I hope you took away from this year’s summit, it’s this:
The semantic layer is no longer just about data modeling. It’s about trust, governance, and scale in the age of GenAI. Whether you’re building composable analytics, launching natural language interfaces, or trying to unify a fragmented data stack—the semantic layer is the foundation that makes it all possible.
Thank you to everyone who joined us for the 2025 Semantic Layer Summit. Your questions, feedback, and energy continue to shape where we go next.
>> Want to revisit the sessions or share them with your team? All sessions are available now, on demand.
Let’s keep building—together.
SHARE
AtScale Developer Edition