holistics-migrate-power-bi

name	holistics-migrate-power-bi
description	Migrate a Power BI semantic model (TMDL + DAX) and reports to Holistics AMQL assets (AML models, datasets, dashboards, AQL/SQL metrics). Use this when converting a .pbix/.pbip project, translating DAX measures, mapping TMDL tables and relationships to Holistics models, or rebuilding Power BI reports as Canvas Dashboards.

Instructions

This skill orchestrates a migration. It does not author AML/AQL by hand. All code authoring, validation, and querying goes through the Holistics CLI (holistics aml ..., holistics mcp ...) and the companion skills in the holistics-development and holistics-reporting plugins.

• Prereqs: Before writing anything, load the setup-amql-development skill first. It prepares the necessary tools such as holistics aml validate, holistics aml compile, holistics sync-code, holistics mcp generate_aql, holistics mcp execute_aql, etc., to let you work efficiently and accurately.
• For every AML/AQL change, defer to develop-amql (models, datasets, dashboards), type references, and write-aql (metrics). They wrap generate_aql / validate_aql correctly.
• NEVER guess AML keys, AQL function names, or operator semantics. Use holistics mcp search_docs '{"question": "..."}' before writing anything novel.
• NEVER hand-write AQL. Use holistics mcp generate_aql '{"dataset_uname": "...", "query": "..."}' (or the write-aql skill) with the original DAX, the model schema, and the business intent.
• Validate every change with holistics aml validate <files> after every edit, and re-run holistics mcp validate_aql '{"aql": "...", "dataset_uname": "..."}' for every metric.
• Value parity is non-negotiable: use holistics mcp execute_aql (or the analyze-data skill) to query Holistics and compare to Power BI for 5–10 dimension combinations per measure.
• Verify warehouse access before starting. Holistics runs live queries, so Excel and other file-only Power BI sources must land in a warehouse first (Postgres, BigQuery, Snowflake, Redshift, etc.). Use holistics mcp list_data_sources '{}' and holistics mcp read_data_source_table_schema to confirm the source landed correctly.
• Disable Power BI's "Auto date/time" before exporting .pbip so LocalDateTable_* artifacts do not appear in TMDL.

When to use

• Converting a .pbix or .pbip project to a Holistics AMQL project.
• Mapping TMDL tables, columns, calculated columns, and relationships to AML models and dataset relationships.
• Translating DAX measures to AQL metrics (preferred) or SQL fallback measures.
• Rebuilding Power BI report pages as Holistics Canvas Dashboards.
• Reviewing a partially migrated project for parity issues.

High-level workflow

Each phase calls out the skills and CLI tools to use.

Phase	Goal	Tools / Skills
0. Setup	CLI ready, repo cloned, dev env selected.	setup-holistics-cli, setup-amql-development.
1. Export	.pbix → .pbip (text).	Power BI Desktop.
2. Inventory	Catalogue tables, measures, relationships, pages, visuals, filters.	rg, find, holistics mcp search_docs for unknown Holistics concepts.
3. Warehouse	Land Power BI imports / M queries in the warehouse.	dbt or SQL views. Verify with holistics mcp list_data_sources + read_data_source_table_schema.
4. Translate	Build models → relationships → dataset → metrics → dashboards.	develop-amql, write-aql, visualize-data. Validate with holistics aml validate + holistics mcp validate_aql.
5. Parity	Run measure-value comparison vs Power BI.	holistics mcp execute_aql, analyze-data. See .
6. Cleanup	Lift visual-level filters, dedupe, document.	develop-amql, manual review.
7. Sync	Push to Holistics Cloud.	holistics sync-code.

Translation order

Always migrate concepts in this order; each step depends on the previous one.

#	Power BI artifact	Holistics target	Skill / Tool	Reference
1	Power Query (M)	dbt or .model.aml (type: 'query')	external dbt; develop-amql for query models
2	Table (TMDL)	.model.aml (type: 'table' or type: 'query')	develop-amql + holistics mcp read_data_source_table_schema
3	Calculated column (DAX)	Dimension in the model/dataset (AQL preferred to SQL)	write-aql + holistics mcp generate_aql
4	Relationships (active + USERELATIONSHIP)	Dataset relationships { } block	develop-amql
5	Semantic model	.dataset.aml	develop-amql + holistics aml validate
6	Measure (DAX)	metric { definition: @aql ... ;; } or measure { definition: @sql ... ;; }	write-aql + holistics mcp generate_aql / validate_aql / execute_aql
7	Report / Dashboard	.page.aml Canvas Dashboard	visualize-data + holistics mcp generate_viz / execute_viz
8	Alerts, subscriptions, embeds, share links	Holistics Alerts, Schedules, Embedded analytics, share links (manual or via REST API)	manual / REST API

Core mental-model shifts

The most common translation patterns:

DAX pattern	AQL replacement
CALCULATE([m], filter)	m | where(filter)
CALCULATE([m], USERELATIONSHIP(...))	m | with_relationships(<aliased join path>)
CALCULATE([m], SAMEPERIODLASTYEAR(...))	m | relative_period(<timestamp>, interval(-1 year))
CALCULATE([m], ALL(table))	m | of_all(<dim_or_model>)
CALCULATE([m], ALLEXCEPT(table, keep))	m | keep_grains(<keep_dim_or_model>)
DIVIDE(a, b)	safe_divide(a, b)
IF / SWITCH	case(when:, then:, else:)
RANKX	rank(order: <metric> | desc(), partition: <dim>)
Calculated table	Query model (Model { type: 'query' … })
Marked Date table	Not required; period functions work on any timestamp column

Full catalogue and AQL execution-order gotchas: .

Recommended project layout

holistics-project/
├── models/
│   ├── tables/
│   │   ├── sales.model.aml
│   │   ├── customer.model.aml
│   │   └── …
│   └── queries/
│       └── active_users.model.aml
├── datasets/
│   └── aw_sales.dataset.aml
└── dashboards/
    ├── executive_summary.page.aml
    └── …

• One Power BI semantic model → one .dataset.aml.
• One Power BI table → one .model.aml.
• One Power BI report → one .page.aml.

Hard rules

• Use the CLI for everything: validation (holistics aml validate), AQL generation (holistics mcp generate_aql), AQL validation (holistics mcp validate_aql), warehouse introspection (holistics mcp read_data_source_table_schema), and live execution (holistics mcp execute_aql).
• Never hand-write AQL. Always go through write-aql / generate_aql, passing the original DAX and intent as context.
• Never invent AML keys, AQL functions, or operator semantics. Read , then call holistics mcp search_docs to confirm.
• Validate after every edit: holistics aml validate <files> for AML; holistics mcp validate_aql for each metric.
• Value parity over visual parity: pick a measure, slice it across 5–10 dim combinations via holistics mcp execute_aql, and compare Power BI vs Holistics. Any difference must be zero or explained.

Discovery commands

Power BI source inventory

# Tables, measures, relationships
rg -l '^table ' <pbip>/<name>.SemanticModel/definition/tables
rg '^\s*measure ' <pbip>/<name>.SemanticModel/definition/tables -A 5
rg 'USERELATIONSHIP|CALCULATE|SAMEPERIODLASTYEAR|ALL\(|ALLEXCEPT|RANKX' <pbip>/<name>.SemanticModel -n

# Report pages and visuals
find <pbip>/<name>.Report/definition/pages -name '*.json'

Holistics target inventory (via CLI)

# Confirm the warehouse source landed
holistics mcp list_data_sources '{}'
holistics mcp list_data_source_schemas '{"data_source_name":"<ds>"}'
holistics mcp read_data_source_table_schema '{"data_source_name":"<ds>","schema_name":"<schema>","table_name":"<table>"}'

# Inspect an existing dataset
holistics mcp list_datasets '{}'
holistics mcp fetch_dataset '{"dataset_uname":"<dataset>"}'

# Lookup canonical values for filters
holistics mcp lookup_values '{"dataset_uname":"<ds>","model_name":"<model>","dimension":"<dim>","lookup_conditions":[{"contain":"<text>"}]}'

# Validate the project
holistics aml validate models/**/*.aml datasets/**/*.aml dashboards/**/*.aml

AQL authoring loop (per measure)

# 1. Generate from DAX + intent
holistics mcp generate_aql '{"dataset_uname":"<ds>","query":"<intent + DAX>"}'

# 2. Validate syntax + semantics
holistics mcp validate_aql '{"dataset_uname":"<ds>","aql":"<aql>"}'

# 3. Execute and compare to Power BI
holistics mcp execute_aql '{"dataset_uname":"<ds>","aql":"<aql>","title":"parity check: <measure>"}'

Validation checklist (per dataset)

• holistics aml validate passes on every .aml (models, datasets, dashboards).
• Every metric passes holistics mcp validate_aql.
• All Power BI relationships are present in the dataset. Flag true/false for active/inactive. Example: relationship(sales.customer_key > customer.customer_key, true).
• Every DAX measure has a metric or measure in Holistics.
• Sample value parity ≥ 99% across 10 dim-combinations, verified via holistics mcp execute_aql (see ).
• Dashboard tab count matches; key visuals reproduce (holistics mcp execute_viz); filters are deduplicated.
• Final push: holistics sync-code to publish to Holistics Cloud.

References

• : Full Power BI ↔ Holistics concept table.
• : TMDL table, column, and relationship translation, including role-playing dimensions.
• : DAX pattern catalogue with AQL execution-order pitfalls and SQL fallbacks.
• : End-to-end migration phases, manual-migration items, and definition of done.
• : Three-layer parity testing strategy.
• Holistics docs: https://docs.holistics.io/docs/from-others/power-bi/conceptual-differences
• Holistics migration guide: https://docs.holistics.io/docs/from-others/power-bi/migrating-to-holistics
• Holistics CLI docs: https://docs.holistics.io/docs/cli

Related skills

This plugin orchestrates skills from the holistics-development and holistics-reporting plugins. Load them on demand:

• setup-holistics-cli — install / authenticate the CLI; provides holistics aml validate, holistics mcp, holistics sync-code.
• setup-amql-development — repo conventions and the validate-as-you-go loop.
• develop-amql — author AML models, datasets, and dashboards.
• write-aql — generate AQL via generate_aql.
• search-docs — look up Holistics syntax and concepts.
• visualize-data — rebuild dashboards via generate_viz / execute_viz.
• analyze-data — run parity comparisons and explain result deltas.