Its own warehouse
A dashboard is rebuilt only on the warehouse its data already sits in.
Migration options
From Tableau to Snowflake or Databricks.
Ask in plain words. The Boreon MCP Layer reads the workbook on your Tableau Server or Tableau Cloud site, finds the Prep flow under it, rebuilds both on the warehouse’s own dashboard and data technology, and checks every number against Tableau before it publishes. No scripts, no hand-written SQL, and go-live in one month.
Go live in one month
- Weeks 1 to 2MVPThe Boreon MCP Layer inside your firewall, with your priority dashboards moved and every number proven.
- Weeks 3 to 4Final deploymentThe rest of your priority workbooks, hardening, sign-off and training.
- Day 30Go liveYour team runs it in chat, and every future move is yours.
What moves where
Each warehouse gets its own technology
Every migration starts on Tableau Server or Tableau Cloud and lands on the warehouse’s own dashboard and data technology.
| To Snowflake | To Databricks | |
|---|---|---|
| Dashboards and workbooks | Streamlit in Snowflake apps, with the layout, marks, colours and filters carried over | AI/BI dashboards with the same measures, filters and colours, and Genie built in |
| Tableau Prep flows | Dynamic tables, one CTE per flow step, or a materialized view where Snowflake allows one | Materialized views, one CTE per flow step |
| The data behind them | Snowflake tables, queried over the SQL API | Unity Catalog tables, queried over the Statement Execution API |
| The proof | Every sheet’s query proven on Snowflake, and every number checked against Tableau | Every sheet’s query proven on Databricks, and every number checked against Tableau |
How a migration runs
Six steps from one line
The customer types one line about a workbook on Tableau Server or Tableau Cloud. The agent runs the rest, and shows each step as it lands.
The one line the customer typedMigrate DBX Platform Latency Calls & Uptime to Databricks
- Read the workbook. Shelves, filters and calculations: three sheets.
- Find the Prep flow. The flow behind its data source: one flow.
- Flow to view. One SQL statement, one CTE per step: 20 million rows in 72 seconds.
- Prove every sheet. Each query runs before publishing: zero SQL errors.
- Check the numbers. Against Tableau’s own answer: 41 of 41 rows.
- Publish. A Databricks AI/BI dashboard, with a fidelity score of 96%.
Measured live
The run, in four numbers
Measured on DBX Platform Latency Calls & Uptime, moved from Tableau to a Databricks AI/BI dashboard.
- sheets identical to Tableau
- 3 / 3
- fidelity score
- 96%
- rows in the view, built in 72 seconds
- 20M
- SQL errors on publish
- 0
The rules it runs by
Nothing ships until it is proven
The engine checks its own work, and every rule below holds on every move.
All or nothing
If one sheet has no faithful match, nothing is published and the agent says why, in plain words.
Numbers first
Averages, distinct counts and medians stay exact under dashboard filters, and every result is checked against Tableau’s own answer.
Proven before it ships
Every query runs on Snowflake or Databricks before publishing, and nothing opens with a SQL error.
Your identity, your roles
SSO roles carry through to both warehouses, and people see what they are allowed to see.
Read-only by default
The only writes are the ones you ask for, and each one is audited.
In every migration
Proof comes with every move
Measured on your own workbooks, and shown to you before you publish.
Fidelity report
A score for every sheet: what was mapped, what was approximated and what was left out.
Parity
Each sheet’s rows compared with Tableau’s own answer.
Proven SQL
Every query runs first, and the dashboard or app opens without a SQL error.
A plain reason
Anything it will not migrate is named, with the reason in plain words.
MVP in two weeks. Go live in one month.
Tell us which dashboards on your Tableau Server or Tableau Cloud site you would move first. We scope them, stand up the Boreon MCP Layer inside your firewall and put a working MVP in front of your team in two weeks, with final deployment and go-live inside the month.