Bring reliable data to every spreadsheet with the dbt Semantic Layer for Excel and Google Sheets

Love them or hate them, spreadsheets aren’t going anywhere. Tools like Google Sheets and Excel remain essential for decision-making across organizations, whether it’s finance forecasting revenue or marketing analyzing campaign performance.

But, as your datasets grow, so do the challenges of ensuring reliable, consistent data across systems. Multiple versions of spreadsheets, inconsistent calculations, and manual imports create confusion, inefficiencies, and ultimately, poor decisions.

For data analysts and engineers, the struggle is just as real—especially when it comes to complex calculations. Preparing data for spreadsheets often means querying subsets from cloud data warehouses, cleaning dimensions, and manually calculating metrics, especially for complex analyses.

Business users need scalable, governed, and easily accessible data to make informed decisions. And data teams need to deliver it efficiently without creating silos, adding pipeline latency, or risking burnout.

The pain of disconnected workflows in spreadsheets

Take an analyst preparing a quarterly sales report. They pull raw data from the warehouse, clean it, aggregate sales by region, and calculate key metrics like average deal size or revenue per salesperson. While the report may look great initially, any new updates to the source data—like adding late-logged sales—render the workbook outdated.

Now, the analyst has to re-extract data, redo the cleaning and aggregations, and reapply the calculations, every single time. This repetitive process can take hours and introduces delays, errors, and inconsistencies.

For example, if the analyst refines how they calculate customer churn or segment product categories, those transformations must be manually reapplied every time new data arrives.

What started as innovative analysis now feels like a repetitive chore. Without a centralized system, their methodology is difficult to track, and any undocumented steps become even harder to reproduce.

These disconnected workflows not only waste time but also make it difficult to maintain accuracy or scale as data needs grow. Teams end up working with outdated extracts and repetitive and siloed processes, leading to bottlenecks and frustration.

A smarter way to work with spreadsheets

With the right approach, spreadsheets can remain a powerful and dependable tool, free from the chaos of inconsistent and outdated data.

Instead of relying on static tables and manual processes**,** the dbt Semantic Layer simplifies how organizations access and work with trusted data by serving as a intermediary layer between your data warehouse and spreadsheets, ensuring users are accessing fresh, governed, and pre-defined metrics to maintain consistency and accuracy.

With built-in integrations for Google Sheets and Excel, business users can pull fresh, trustworthy data directly from cloud platforms like Snowflake, BigQuery, and Redshift. This eliminates the need for manual imports, rigid OLAP cubes, and complex Multidimensional Expressions (MDX) queries (the language Excel uses to query cubes). Traditionally these methods require extensive infrastructure, pre-aggregations, and frequent refreshes, leading to stale data and high maintenance costs. Instead, the dbt Semantic Layer bypasses MDX entirely, and dynamically translates spreadsheet queries into SQL under the hood, so teams can query even their most complex calculations in real-time without creating extra static tables or writing custom queries.

This means no more hardwired definitions or misaligned metrics—just reliable, up-to-date data that teams can trust. Whether calculating revenue growth, analyzing customer churn, or building pivot tables, dbt simplifies workflows and ensures consistency across every tool.

How the dbt Semantic Layer works for Excel and Google Sheets

1. Consistent, always up-to-date metrics

dbt Cloud’s transformation capabilities, combined with the dbt Semantic Layer, provide a unified and trusted foundation for organizational data. dbt Cloud allows teams to transform raw data from cloud data platforms into clean, version-controlled, analytics-ready datasets, while the dbt Semantic Layer centralizes and standardizes business metrics and definitions on top of these datasets, making them accessible across tools like Excel and Google Sheets.

Metrics such as “year-over-year revenue growth” or “net retention rate” are defined in the dbt Semantic layer on top of dbt models and accessed across tools. This creates a single source of truth allowing teams to work with accurate and consistent data, whether they’re using Google Sheets, Excel, or any other connected application.

And because spreadsheet queries run directly against the warehouse, reports automatically update with the latest data—no more exports or duplicate work.

2. Seamless integrations with Excel and Google Sheets

dbt connects directly to your cloud data warehouse, eliminating the need for OLAP cubes, MDX queries, or manual imports. With an intuitive built-in query builder, users can easily select pre-defined metrics, apply filters, and retrieve the exact the data they need in seconds. This ensures that pivot tables in Excel or custom formulas in Google Sheets function seamlessly with accurate, up-to-date, governed data.

By delivering clean, consistent datasets directly into these tools, dbt empowers teams—technical and non-technical alike—to confidently perform analyses without relying on manual and siloed updates to business logic.

3. Fast insights for complex data needs

For teams handling large datasets or complex financial calculations, the dbt Semantic Layer enables high-performance multidimensional queries directly on live warehouse data. Business users can build pivot tables, slice and dice across dimensions, and collaborate with consistent metrics—all without relying on pre-aggregated cubes or static tables.

Built-in query optimization features in the dbt Semantic Layer enhance performance with features like:

• Common query caching, which reduces latency by storing frequently accessed results.
• Pushdown computations that offload complex calculations to the warehouse, leveraging its processing power, while filtering
• And query pruning which ensures only necessary data is queried, improving speed and efficiency.

This approach ensures faster, more accurate insights and reduces the overhead of managing and refreshing static data extracts. Teams can focus on analysis rather than infrastructure, driving better outcomes.

4. Governed data that aligns teams

With centralized governance, dbt ensures all teams work with consistent, accurate data, reducing the chaos of spreadsheet sprawl. By providing a single source of truth for business logic, the dbt Semantic Layer eliminates manual processes and repetitive maintenance, allowing allowing anyone in the business, from finance users to analysts, to trust that their reports are aligned across the organization.

This governance empowers teams to self-serve their data needs with ease, enabling faster insights and freeing analysts to focus on strategic tasks rather than firefighting data inconsistencies. With dbt, teams can operate with greater confidence and reliability.

Make quality data accessible to everyone

The dbt Semantic Layer redefines what’s possible with spreadsheets. With built-in integrations, automatic updates, and centralized metric governance, teams are empowered to move faster, collaborate more effectively, make better decisions, and can finally trust their spreadsheets.

This is the new standard for trusted, scalable spreadsheet workflows. Book a demo today!