Create import connection Microsoft power bi to big Query

you can create an Import query with a GCP BigQuery connection in Power BI, but there are important limitations and considerations depending on the Power BI version and data connector used.





✅ 

Import Mode with BigQuery – Supported



Power BI supports Import mode from Google BigQuery using the built-in connector, as long as:



  • You’re using Power BI Desktop
  • You have credentials (usually Google account OAuth2 or service account)
  • The dataset isn’t too large, since Import loads data into Power BI’s in-memory model






🔁 

Steps to Use Import Mode with BigQuery




  1. Open Power BI Desktop
  2. Go to Home > Get Data > More…
  3. Choose Google BigQuery (under Database category)
  4. Authenticate (with Google account or service account key)
  5. Navigate through your GCP project > Dataset > Tables
  6. In the navigator:

  8. Select tables or views
  9. At the bottom, choose Load (this is Import mode) or Transform Data to go through Power Query

  11. Power BI will load the data into memory and store it in the .pbix file






🔍 Difference Between Import and DirectQuery


Feature

Import

DirectQuery

Performance

Fast for analysis (in-memory)

Slower – queries sent to BigQuery live

File Size

Limited by RAM (Power BI file grows)

Lightweight – no large data stored

Refresh

Needs scheduled refresh (via Gateway)

Live data, but limited transformations

Transformations

Full Power Query and DAX available

Limited – many transformations restricted






⚠️ Considerations and Limitations



  • Data size: BigQuery tables can be huge; import only what you need.
  • Cost: Importing large data can incur BigQuery query costs (pay-per-query).
  • Gateway requirement: Scheduled refresh in Power BI Service requires on-premises data gateway, even for cloud sources like BigQuery (unless using personal gateway or VNet).
  • Query Folding: Power Query may or may not fold your transformations back to BigQuery — if not, performance and cost may suffer.






💡 Tip: Reduce Query Cost and Improve Performance



  • Use Custom SQL or BigQuery views to pre-aggregate/filter data before loading
  • Use incremental refresh (for large tables with date-based partitions)
  • Keep imports small or use DirectQuery/Hybrid mode when live data is essential



From Blogger iPhone client

Microsoft Power BI - Semantic Models

The semantic model in Power BI (also called the tabular model or data model) is designed primarily for data analysis and consumption, not for complex data transformation. Here’s why it has limitations on data transformation:





🔹 1. 

Performance and Optimization Focus



  • Semantic models are optimized for fast querying, aggregations, and visualizations.
  • Allowing heavy transformations at query time would slow down performance, defeating the purpose of a semantic model.






🔹 2. 

Design Separation: Transform vs Model



Power BI follows a separation of concerns:


  • Power Query (M) handles data transformation and shaping (ETL).
  • Semantic model (DAX) handles calculated columns, measures, relationships, and business logic.



This ensures:


  • Cleaner models
  • Reusability
  • Efficient refresh and query performance






🔹 3. 

DAX Is Not Meant for ETL



  • DAX (used in the semantic model) is designed for calculated logic on already loaded data, not for:
  • Complex joins
  • Row-level transformations
  • Column reshaping or unpivoting

  • These tasks are meant to be done in Power Query or upstream ETL tools (like Alteryx, SQL, etc.).






🔹 4. 

Storage Engine Constraints



  • The semantic model uses VertiPaq, an in-memory columnar storage engine.
  • VertiPaq is efficient only if data is clean and structured. Transformations can:
  • Increase model size
  • Reduce compression efficiency
  • Slow down queries






🔹 5. 

Governance and Maintainability



  • If complex transformations are done within the semantic model:
  • It becomes harder to audit, manage, or debug.
  • Data lineage becomes less transparent.
  • Data governance is weakened.






✅ What the Semantic Model 

Should Do



  • Define relationships
  • Add business logic (measures, KPIs, hierarchies)
  • Handle role-level security
  • Enable efficient slicing and dicing of clean, transformed data






🧠 Best Practice



Perform all heavy transformations in Power Query or a data warehouse. Use the semantic model only for modeling and business logic.


From Blogger iPhone client
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