Databricks entering the Excel connectivity market is meaningful market validation. It confirms what Exponam has built a company around: governed, business-user access to lakehouse data from Excel is not an edge case or a transitional requirement. It is a permanent, strategically important capability that enterprises need.

We have spent three years in production enterprise deployments solving exactly this problem. Databricks has spent considerably more on engineering and market resources than we have. So we want to give an honest account of where the new product performs, where it falls short, and what the right choice is depending on your situation.

What Databricks built

The Databricks Excel Add-in uses the SQL warehouse endpoint — the same compute layer that powers Databricks SQL notebooks and BI tool connections — to query data and return results into Excel. Users browse Unity Catalog, apply filters, and import data into worksheets through a task pane interface. They can also write SQL directly using =DATABRICKS.SQL() cell functions. Unity Catalog governance applies throughout. The product is a web-based Office Add-in, running on Windows, macOS, and Excel for the web using the same cross-platform JavaScript framework.

For a first-version public preview, it is a credible product. It will serve some organizations well — particularly small teams already operating inside a Databricks workspace who need occasional data in a spreadsheet.

Where Exponam.Connect is different

The foundational architectural difference is the data retrieval path. Exponam.Connect offers users a choice: access data via Delta Sharing — the open protocol that retrieves compressed Parquet files directly from cloud object storage, bypassing Databricks compute entirely — or via a SQL warehouse endpoint when full SQL syntax is required. The SQL endpoint path, currently in private preview, gives users arbitrary joins, window functions, and subqueries. The Delta Sharing path gives them speed and zero compute cost.

Databricks’ add-in uses the SQL endpoint only — there is no zero-compute access mode. Every data pull consumes DBUs.

On Windows, Exponam.Connect uses the VSTO framework — the native Microsoft COM-based integration layer — rather than the web-based Office Add-in model Databricks uses on all platforms. The practical result: data writes into Excel are significantly faster for large datasets, as the trial results below demonstrate.

A direct comparison

The table below covers the dimensions that matter most in an enterprise evaluation. We have tried to be accurate about both products, including areas where they are genuinely equivalent or where Databricks holds an advantage.

What we observed in direct testing

In testing conducted for this comparison, both products were run against the same large enterprise-scale dataset. The results were unambiguous.

Dataset: a large-scale enterprise transaction table available as a Delta Sharing trial dataset, containing approximately 2.88 million rows. Results from a single test run on standard hardware; individual results may vary by network conditions, warehouse configuration, and add-in version.

The Databricks add-in’s error was non-descriptive — no indication of whether it hit a row limit, a memory ceiling, or a query timeout, and no guidance on how to proceed. Organizations planning to use the add-in for large datasets should validate against their specific data volumes before deployment.

Designed for different users

The two products reveal their intended audiences the moment you use them. This is not a criticism of either — it is a useful signal for organizations deciding which to deploy and to whom.

Exponam.Connect was designed by business users for business users. The ribbon integration, task pane layout, and interaction model are all built to mirror what Excel users already know. Filters are applied exactly as they are on a native Excel sheet — same gestures, same mental model, no new vocabulary to learn. A finance analyst or operations manager can be productive within minutes of installation, without training or documentation. The design assumption is that Excel is home, and the add-in should feel like a natural extension of it.

The Databricks Excel Add-in was designed by technologists. Even using the basic table-select option — the simplest path through the product — the interface looks and feels like a BI platform’s report modification UI. It is parameter-driven, query-centric, and structured around concepts that are second nature to a data engineer or SQL analyst but unfamiliar to the typical Excel business user. There is nothing wrong with this for its intended audience. A Databricks-credentialed analyst who spends time in notebooks and SQL editors will feel at home. Someone who does not will not.

The cost question, with real numbers

The Databricks add-in carries no license fee, which looks attractive on first comparison. During testing, we ran four to five query imports in a single working day — casual, representative analyst use. The result: 16 DBU consumed, equating to $14.58 at the $0.70/DBU SQL Serverless list rate. For one analyst. One day. Light load.

Against that, here is what Exponam.Connect costs at the same user counts:

† Conservative floor estimate. ‡ Based on observed trial rate of 5 DBU/analyst/day at $0.70/DBU (SQL Serverless list) × 20 working days. Actual costs vary by query frequency, warehouse type, dataset size, and contracted DBU rates. Formula recalculation in shared workbooks can add further unplanned consumption.

At the observed trial rate, Exponam.Connect is 7× cheaper at 100 users, 14× cheaper at 1,000, and 70× cheaper at 10,000. Even the conservative $25/user floor produces multiples of 2.5×, 5×, and 25×. One caveat: for teams of fewer than 100 users, Exponam.Connect’s $1,000/month minimum is a meaningful consideration, and in that range the Databricks add-in’s absence of a license fee is a genuine advantage — if DBU consumption is carefully monitored.

Where Databricks has a genuine advantage

We said we would be honest, so here it is.

Small teams with existing Databricks credentials. If your team is fewer than 100 people, already operating inside a Databricks workspace, and primarily running SQL-scale queries rather than million-row imports, the Databricks add-in is reasonable. No additional license, solid SQL functionality, the same Unity Catalog governance your team already uses.

Mac-first organizations. On macOS, both products use the Office Web Add-in framework and the picture is more balanced. Exponam.Connect’s Delta Sharing cost advantage still applies, but the VSTO performance advantage does not. And Exponam.AI and the advanced SQL editor are not yet available on Exponam.Connect’s Mac edition. Mac-heavy deployments should evaluate both products carefully.

Platform confidence. Databricks is a $62B company releasing a first-party product. Enterprises weigh vendor stability and support structure, and Databricks carries weight in that evaluation.

What comes next from Exponam

Databricks’ entry does not change our roadmap — it confirms it. Three capabilities currently in development:

Natural language / AI query. Users will be able to describe their data need in plain English and receive governed, reproducible results — with a private or BYO LLM option for regulated environments where data cannot leave controlled infrastructure.

Automatic path optimization. AI-driven routing will select between Delta Sharing and the SQL endpoint on each query, optimizing automatically for cost and performance. Users won’t need to choose — the system will.

Multi-cloud expansion. Snowflake and Azure Fabric support are on the near-term roadmap. Databricks will build a good connector for Databricks data. They will not build one for Snowflake. We will.

The bottom line

Databricks entering the space is good news for the market. It validates the problem and raises awareness that governed, no-code lakehouse data access from Excel is available. Some of that awareness will land on their product. Some will land on ours.

For organizations with more than 100 users, Windows-primary deployments, cost sensitivity at scale, external data sharing needs, or multi-cloud environments: Exponam.Connect is the stronger choice on the merits. For small teams already inside Databricks’ ecosystem who need light-duty access: the Databricks add-in is a reasonable starting point — with the caveat that data volume limits and DBU consumption should be validated before any broader rollout.

The full technical comparison — covering installation, architecture, performance, cost, governance, usability, and roadmap in depth — is available below.