Welcome!

What is a semantic layer?

A semantic layer is a translation layer between raw data and people. Instead of forcing business users to understand database schemas, table joins, and SQL, a semantic layer lets them work with concepts they already know: revenue, region, customer segment, year-over-year growth.

Think of it this way:

Without a semantic layer: Each tool figures out the data on its own. Definitions drift. Nobody is sure which number is right.
With a semantic layer: Business logic is defined once, governed centrally, and reused everywhere. Everyone works from the same playbook.

What problem does AtScale solve?

Most organizations store their data in cloud platforms like Snowflake, Databricks, or BigQuery. To make sense of that data, teams use BI tools like Tableau, Power BI, or Excel — and increasingly, AI applications and notebooks.

The problem is that each of those tools needs to understand the data independently. Every dashboard, report, or AI agent has to define what “revenue” means, how “active customer” is calculated, or which tables to join. Over time, this leads to:

Conflicting numbers — two dashboards showing different revenue figures because each defines the metric differently.
Duplicated work — analysts rebuilding the same business logic in every tool.
Governance gaps — no single place to enforce who can see what data or which definitions are approved.
AI that guesses — AI tools working from raw table schemas with no understanding of business meaning.

AtScale solves this by giving your organization one place to define business logic — metrics, dimensions, hierarchies, relationships, and calculations — and making those definitions available to every tool that needs them.

How does AtScale actually work?

AtScale connects to your existing cloud data platform and exposes a governed semantic model to the tools your teams already use. It doesn't move or copy your data — it queries it where it lives.

Here's the basic flow:

Your data platform — Snowflake, Databricks, Google BigQuery, Redshift, InterSystems IRIS, or PostgreSQL — stores the raw and modeled data.
AtScale defines what that data means in business terms and optimizes how queries are run against it.
Your tools — Power BI, Tableau, Excel, Looker, Google Sheets, or supported AI applications such as ChatGPT, Claude, and Databricks Agent Bricks — connect to AtScale and get consistent, governed answers.

An important distinction: AtScale is not a BI tool — it doesn't build dashboards or visualizations. And it's not a data warehouse — it doesn't store your data. It's the layer in between that makes everything else work together consistently.

What can you do with AtScale?

If you need to…	AtScale helps by…
Get consistent metrics across dashboards	Defining measures like revenue, margin, or churn once and sharing them across all tools
Let business users explore data safely	Providing governed self-service access with centralized security and approved definitions
Speed up slow queries on large datasets	Optimizing and accelerating analytical queries against cloud data platforms
Feed business context to AI applications	Giving AI agents defined metrics, relationships, and business rules instead of raw schemas
Use multiple BI tools without conflicts	Acting as a shared semantic layer so Tableau, Power BI, Excel, and others all use the same logic

Who is AtScale built for?

AtScale is designed for organizations where multiple teams, tools, or AI workflows need to use the same business logic consistently. You'll encounter it if you're a:

Business analyst looking for reliable, self-service access to governed metrics

BI developer maintaining dashboards and reports across tools like Power BI or Tableau
Data platform engineer trying to reduce duplicated logic and improve governance
Analytics engineer building and maintaining reusable semantic models
Data scientist or AI engineer who needs trusted, explainable business context for models and agents
Support engineer troubleshooting issues across the data platform, semantic layer, and BI tool stack

Where does AtScale fit in the bigger picture?

Layer	Examples	AtScale's role
Data platforms	Snowflake, Databricks, Google BigQuery, Redshift, InterSystems IRIS, PostgreSQL	Connects to these and queries data in place
BI and analytics tools	Power BI, Tableau, Excel, Looker, Google Sheets	Provides shared metrics and dimensions to these tools
AI and data science	ChatGPT, Claude, Databricks Agent Bricks	Supplies governed business context and consistent metrics
Governance and catalogs	Metadata catalogs, access-control systems	Supports consistent definitions and policy-aware access

Common questions

Does AtScale move or store my data?

No. AtScale queries your data where it already lives. It provides semantic modeling, governance, and performance optimization on top of your existing cloud data platform.

How is AtScale different from a BI tool?

BI tools build dashboards and visualizations. AtScale provides the shared semantic layer underneath those tools, so multiple BI tools, spreadsheets, and AI applications all use the same governed definitions.

How does AtScale help with AI?

AI systems need more than raw tables — they need clear definitions for metrics, joins, time logic, and business rules. AtScale provides that governed context so AI agents produce more consistent, explainable results.

Why does governance matter?

Without governance, every tool defines its own version of the truth. AtScale applies governance through a shared layer — approved definitions, security rules, and access controls — instead of relying on each tool to enforce its own.

Key takeaways

AtScale is a universal semantic layer platform — it sits between your data and your tools.
It lets you define metrics and business logic once and reuse them everywhere.
It queries data in place — no copying, no extracts, no separate data store.
It supports BI tools, spreadsheets, notebooks, and AI applications from one governed layer.
It helps organizations trust their numbers, simplify governance, and make AI more reliable.