Turn a Databricks Genie Answer Into a Publishable Spreadsheet

If you want to turn a Databricks Genie answer into a spreadsheet readers can explore, start with a defined output—not just a useful chat response. For example, a public library analysis might need one row per branch per month, with loan counts, a source reference and a clear reporting period.

The practical workflow is: ask a precise question, review the generated query, export an approved result, then publish that reviewed file. Genie’s spreadsheet announcement points to another analysis interface, but it does not remove the need for a separate public-release check.

What “Databricks Genie spreadsheet” means now

On September 24, 2026, Databricks announced its acquisition of Row Zero. It said Genie will gain a native, governed spreadsheet experience for exploring data, modeling scenarios and collaborating, backed by Genie Ontology, Unity Catalog and Unity Gateway. The announcement describes native integration in future tense; it is not, by itself, confirmation that the interface is enabled in your workspace. Source: Databricks announcement.

Separately, as of October 29, 2026, Row Zero documents a built-in Databricks connector supporting OAuth or a personal access token, auto-updating spreadsheets, SQL queries and write-back. Its per-user row-level access controls are specifically described in connection with Databricks OAuth. Those documented connector capabilities should not be treated as proof of an identical Genie-native interface. Source: Row Zero’s Databricks integration.

For a publication workflow, Databricks’ AWS documentation already describes Genie Agent result tables and CSV downloads. Genie Agents were formerly called Genie Spaces. The steps below use that documented path rather than assume a new spreadsheet menu exists. Source: Genie Agent documentation.

1. Define the table before asking Genie

Use only data authorized for public release. For the library example, that means an approved aggregate dataset—not patron records or borrowing histories.

An illustrative prompt is:

Using the approved public-release library loan table, return one row per branch per calendar month for January through September 2026, inclusive. Include branch code, branch name, month and total loans. Sort by month, then branch code.

Adapt the table name and definitions to your actual source. Specify what a “loan” includes, whether renewals count, and which date determines the month. Choose a reporting period that excludes incomplete months unless you intend to label them as partial.

A publication-ready structure could be:

Column	Meaning
branch_code	Stable branch identifier, preserved as text
branch_name	Readable branch label
month	Reporting month, consistently written as YYYY-MM
loans_count	Count under the documented loan definition
source_reference	Public source URL or release identifier
data_as_of	Source snapshot date

This is a proposed schema, not an actual library dataset. Add provenance columns during preparation if they are not part of the query result.

2. Check the answer behind the table

In a Genie Agent result table, Show code lets you inspect the generated SQL. Databricks also recommends carefully reviewing responses because Genie can produce errors. Source: response review guidance.

Before exporting, check:

• Source: Did the query use the intended approved table?
• Row definition: Is each branch-month combination unique?
• Filters: Are the dates and exclusions correct?
• Joins: Could matching multiple records inflate the counts?
• Missing values: Does a missing branch-month mean zero, unavailable data or a closed branch?
• Totals: Do monthly sums match a separately verified source total?

If you cannot verify the SQL yourself, ask the agent author or a qualified analyst to review it before release.

Save the reviewed SQL and source snapshot reference alongside the export in your internal review records. A prompt alone is not a reproducible specification: Genie can return different outputs for the same wording, and the underlying data can change. Source: Genie conversation guidance.

3. Export a reviewed snapshot

The AWS Genie Agent documentation says to use the download button in the response to download results as CSV. Treat that file as a snapshot, not a live connection. Source: CSV download instructions.

Open the exported file and verify its row count, headers, identifiers and numeric values. Confirm that it contains the intended result rather than a narrowed subset. Keep missing values distinct from genuine zeros, and avoid mixing units in one measure column.

Permission to query data is not permission to publish it. Obtain release approval, check reuse rights, and exclude personal information, confidential fields and aggregates that could expose individuals. If export is restricted, ask the data owner for an approved extract rather than trying another route around the restriction.

For file-level checks, use the CSV preparation guide.

4. Publish the file with its context

TablePage accepts CSV, TSV, XLSX and XLS files and generates a public dataset page with a shareable link and a filterable table. Anyone can explore the page without signing up—so upload only the approved public-release file.

Prepare accompanying publication notes with the public source, measure definition, reporting period, snapshot date and known gaps. Verify that those notes are visible wherever you share the dataset; do not assume a CSV carries the chat’s context with it. Keep internal source paths and other confidential review details out of the public notes.

Finally, inspect the public page as a reader: check that month values sort correctly, branch labels remain intact and filters show the expected rows. Describe the page as a dated snapshot unless you have established and verified a separate update process.

A governed spreadsheet serves authorized analysis. A public dataset page serves an outside reader. The important handoff is the reviewed, documented table between them.