Open Data Infrastructure
Migrating Your BI and Semantic Layer to Open Infrastructure
Semantic-layer migration is not a dashboard cleanup. It is the work of making metrics, entities, policy, and lineage survive a platform change.
The scariest part of a data platform migration is usually not moving the data. It is proving the numbers still mean the same thing.
Metrics are where migrations get personal
A table can move successfully and still break the business. If revenue, active users, churn, margin, or fulfillment rate changes after a migration, nobody wants to hear that the Parquet files look correct.
BI and semantic-layer migration is the work of preserving meaning. That means metrics, dimensions, joins, filters, access rules, freshness expectations, and lineage all need explicit owners.
Core idea: the semantic layer is portable only when business meaning is versioned, tested, and connected to open data contracts.
Inventory the meaning before the tools
Start by mapping the semantic estate:
- critical metrics and their definitions
- dashboards and workflows that use each metric
- entity definitions and join paths
- filters embedded in BI tools
- row-level and column-level access assumptions
- freshness and backfill behavior
The hidden logic matters most. Migrations fail when teams move version-controlled models and forget the calculations living in dashboards, extracts, notebooks, and analyst muscle memory.
Move toward a contract-first semantic layer
A contract-first semantic layer treats definitions as code and operational behavior. The metric definition is one part. The source table, grain, filters, ownership, policy, and lineage are also part of the contract.
Open table formats and catalog interfaces help because they keep the physical and logical layers from collapsing into one vendor system. Iceberg views and semantic-layer tools are not interchangeable, but they point toward the same requirement: business meaning needs a portable representation.
Validation needs parallel runs
Do not cut over a semantic layer by faith. Run old and new paths in parallel. Compare metric outputs at the same grain, with the same filters, over meaningful historical windows. Investigate differences until they are explained, accepted, or fixed.
Version the validation results. A migration that cannot explain why numbers changed is not done. It is waiting for an executive review to discover the problem.
Agents need context beyond metrics
BI users can ask a human why a metric looks weird. Agents need machine-readable context. They need source, lineage, freshness, policy, allowed actions, and entity relationships.
That is why semantic-layer migration should not stop at dashboard parity. The open infrastructure target should support a context graph over governed data, not just a new place to run the old charts.
Sources to start with
Use semantic-layer, transformation, view, and lineage docs to separate business meaning from one platform boundary.