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Boreon MCP Layer · Migrations

Move Tableau to Databricks in one conversation.

The Boreon MCP Layer sits between your Tableau Server or Tableau Cloud site and Databricks. Ask for a workbook and the agent reads it, turns the Prep flow underneath into a materialized view, rebuilds the dashboard as an AI/BI dashboard with Genie built in, and checks every number against Tableau before publishing. Nobody writes a script or a line of SQL.

Book a working session See it live at ai.boreon.com

Measured live on a real Tableau workbook: DBX Platform Latency Calls & Uptime, moved to Databricks.

3 / 3
sheets identical to Tableau
96%
fidelity score
20M
rows in the view, built in 72 s
0
SQL errors on publish

Watch a real migration run.

Your central LLM portal

Migrate DBX Platform Latency Calls & Uptime to Databricks

  1. Read the workbook Dashboard “Databricks Latency Dashboard” · 3 sheets · shelves, filters, calculations 3 sheets
  2. Found the Prep flow behind it Data source bi_usage_events_databricks is a flow output on Databricks 1 flow
  3. Flow to materialized view One SQL statement, one CTE per flow step CREATE MATERIALIZED VIEW bi.usage_events_mv AS WITH clean AS (…), joined AS (…) SELECT … FROM joined 20M rows · 72 s
  4. Every sheet proven on Databricks Each sheet's query runs before anything is published 0 SQL errors
  5. Numbers checked against Tableau VizQL Data Service parity, row by row 41 / 41 rows
  6. Published as a Databricks AI/BI dashboard Fidelity shown per sheet 96%

Published. The customer only typed the first line.

One layer that speaks Tableau and Databricks.

The layer reads each side through its own APIs and keeps a map of how Tableau fields line up with warehouse columns. It only writes when you ask, and it logs every write.

Tableau

  • Tableau Server or Tableau Cloud, your own site
  • Workbooks and dashboards (.twb / .twbx)
  • Published data sources (.tds)
  • Tableau Prep flows (.tfl)
  • VizQL Data Service and the Metadata API
  • PAT, JWT or a central service account

Boreon MCP Layer

Behind the Data & AI Portal

Databricks

  • Your workspace and Unity Catalog
  • Unity Catalog tables and SQL warehouses
  • AI/BI dashboards, with Genie built in
  • Materialized views from Prep flows
  • Statement Execution API for every query
  • PAT, OAuth or a service principal

What moves, and what it becomes.

Workbook to AI/BI dashboard

Tableau dashboards

Sheets become Lakeview widgets with the same measures, filters, colors, dual axes and number formats, and open in the portal with Genie built in.

.tfl to materialized view

Tableau Prep flows

Each flow step becomes a CTE in one SQL statement, created as a Databricks materialized view.

Flow, view, workbook

The whole chain

A workbook fed by a Prep flow moves as one job. The view is created and verified first, then the dashboard is re-pointed to it.

The rules it works by.

  1. 01 All or nothing

    A dashboard is only rebuilt on the Databricks tables its data already lives in. If a single sheet can't be matched, nothing is published, and you get the reason.

  2. 02 Numbers first

    Averages, distinct counts and medians stay exact under dashboard filters, and every result is checked against Tableau's answer.

  3. 03 Proven before it ships

    Every sheet's query runs on Databricks before publishing, so the dashboard opens without SQL errors.

  4. 04 Your identity, your roles

    SSO roles carry through to Databricks, and embedded dashboards open without a second sign-in.

  5. 05 Read-only by default

    It only writes what you ask for, a published dashboard or a new view, and logs each one.

Which Tableau dashboard would you move first?

Start there. It becomes the first thing we move, checked number by number against Tableau, and the portal it lands in stays yours.

Book a working session See it live at ai.boreon.com