Direct vs Import mode in Power BI use Cases

Absolutely—this is a classic Power BI architecture question, and getting it right saves a lot of pain later. I’ll break this down cleanly and practically, with clear use cases for Import vs DirectQuery, plus a quick decision guide at the end.





1. Import Mode – Use Cases



What it is

Data is loaded into Power BI’s in-memory VertiPaq engine.



✅ Best Use Cases for Import Mode




1. Small to Medium Data Volumes



  • Datasets up to hundreds of millions of rows (compressed)
  • Fact tables with historical data
  • Dimensional models (star schema)



Examples


  • Finance GL, AP/AR
  • HR data
  • Sales history
  • Budget vs actuals






2. High-Performance Dashboards



  • Fast slicers, visuals, drill-downs
  • Complex DAX measures
  • Executive dashboards



Why


  • In-memory = sub-second response time






3. Complex Transformations & Modeling



  • Heavy Power Query (M) transformations
  • Calculated columns
  • Many-to-many relationships
  • Advanced DAX logic



Examples


  • KPI calculations
  • Time intelligence
  • Rolling averages
  • Scenario modeling






4. Data Sources That Don’t Support DirectQuery Well



  • Excel
  • CSV / flat files
  • SharePoint files
  • APIs
  • Some legacy systems






5. Cost-Controlled Environments



  • Reduces load on source systems
  • Ideal when database query costs are high



Examples


  • Cloud databases with per-query pricing
  • Production ERP systems






6. Offline or Limited Connectivity Scenarios



  • Users accessing reports with intermittent connectivity
  • Reports shared as PBIX files






7. Data Governance & Snapshot Reporting



  • Point-in-time reporting
  • Month-end or quarter-end snapshots
  • Audit and compliance reporting






8. Row-Level Security (RLS) Heavy Models



  • Large RLS user bases
  • Complex security logic






❌ Limitations of Import Mode



  • Data freshness depends on refresh schedule
  • Dataset size limits (especially on shared capacity)
  • Refresh windows and failures






2. DirectQuery Mode – Use Cases



What it is

Power BI sends queries directly to the source system in real time.





✅ Best Use Cases for DirectQuery




1. Near Real-Time or Real-Time Reporting



  • Data must be current within seconds/minutes
  • Streaming or operational dashboards



Examples


  • Call center dashboards
  • Manufacturing sensors
  • Logistics tracking
  • Fraud monitoring






2. Very Large Datasets (TB / PB Scale)



  • Billions of rows
  • Data too large to import



Examples


  • Clickstream data
  • IoT telemetry
  • Transaction logs






3. Source Systems Designed for Analytics



  • SQL Server
  • Azure SQL
  • Azure Synapse
  • Snowflake
  • BigQuery



These systems:


  • Handle concurrency
  • Optimize query execution






4. Data Residency / Compliance Constraints



  • Data cannot be copied
  • Regulatory or legal restrictions



Examples


  • Government data
  • Sensitive healthcare or financial data






5. Centralized Semantic Layer Strategy



  • Power BI used mainly as a visualization layer
  • Business logic lives in:
  • Database views
  • Stored procedures
  • dbt models






6. Frequent Data Changes



  • High data volatility
  • Refresh windows are impractical






7. Cost or Storage Constraints in Power BI



  • Avoids dataset size limits
  • Reduces memory consumption in Premium capacity






❌ Limitations of DirectQuery



  • Slower visuals (query latency)
  • Limited DAX and Power Query features
  • Heavy dependency on source performance
  • Can overload source systems
  • RLS can become expensive






3. Import vs DirectQuery – Side-by-Side Summary


Your organization's data cannot be pasted here.






4. Composite Models (Hybrid) – Best of Both Worlds



You don’t have to choose just one.



Common Composite Use Cases



  • Import dimensions + DirectQuery fact
  • Historical data imported + real-time data DirectQuery
  • Aggregations in Import + detail in DirectQuery



Examples


  • Finance: historical GL imported, current-day transactions live
  • Retail: sales aggregates imported, order-level details live






5. When NOT to Use Each Mode




Avoid Import When



  • Data must be real-time
  • Dataset refresh exceeds capacity limits
  • Source data is constantly changing




Avoid DirectQuery When



  • You need complex DAX
  • Users expect lightning-fast visuals
  • Source system is not optimized for analytics






6. Practical Decision Framework



Ask these questions:


  1. Do users need real-time data?
  3. Yes → DirectQuery
  4. No → Import

  6. Is the dataset too large to fit comfortably?
  8. Yes → DirectQuery or Aggregations
  9. No → Import

  11. Is performance critical?
  13. Yes → Import


  16. Are there compliance constraints?
  18. Yes → DirectQuery


  21. Do you need advanced modeling & DAX?
  23. Yes → Import







7. Quick Rule of Thumb



Default to Import. Use DirectQuery only when you must.


Power BI was designed to shine with Import mode—DirectQuery is powerful but should be used deliberately.




If you want, I can:


  • Map this to your finance / BI cloud setup
  • Recommend a composite model architecture
  • Compare Power BI vs Looker / Tableau for DirectQuery-heavy workloads



Just tell me the environment 👌


From Blogger iPhone client

Classic ML AI frameworks

Yes — but not in exactly the same way as Delta Lake.

Classic AI/ML doesn’t have one single universal layer identical to “Delta Lake”, but it does have a set of frameworks that play similar roles for models, data, and pipelines. The closest equivalents depend on what part of Delta Lake you are comparing to.


Below is a clear mapping.





First: What Delta Lake is in Data Engineering



Delta Lake provides:



  • A reliable storage layer
  • Versioning of data
  • Schema enforcement
  • ACID transactions
  • Reproducibility



So your question is basically:

👉 “Is there a similar standardized layer for AI models?”





✅ The AI/ML equivalents (by function)




1) If you mean: “A Delta Lake for ML data” → Feature Stores



This is the closest analogy for AI to Delta Lake.


Examples:



  • Feast
  • Google Vertex AI Feature Store
  • Databricks Feature Store
  • AWS SageMaker Feature Store



These provide:



  • Versioned features
  • Consistent training vs. inference data
  • Data governance
  • Reproducibility



👉 Think of this as “Delta Lake for ML features.”





2) If you mean: “A Delta Lake for models” → Model Registry



Instead of storing tables, you store models.


Examples:



  • MLflow Model Registry
  • Vertex AI Model Registry
  • SageMaker Model Registry



These provide:



  • Model versioning
  • Staging → Production lifecycle
  • Audit trail
  • Rollback capability



👉 This is the closest “governance layer for AI models.”





3) If you mean: “A framework like Spark + Delta Lake” → End-to-End ML Platforms



These combine training, tracking, and deployment:



  • MLflow
  • Kubeflow
  • TensorFlow Extended (TFX)
  • Vertex AI Pipelines
  • Ray + Ray Serve



These act like:



  • Spark = execution engine
  • Delta Lake = reliability layer



But in ML form.





4) If you mean: “Versioning like Delta Lake” → Data & Experiment Tracking



Tools that track versions of data, code, and experiments:



  • DVC (Data Version Control)
  • MLflow Tracking
  • Weights & Biases (W&B)



These ensure:



  • You can reproduce past model results
  • You know which data trained which model


Delta Lake Role

AI/ML Equivalent

Reliable data layer

Feature Store (Feast, Vertex AI FS)

Table versioning

DVC / MLflow tracking

Governance

Model Registry (MLflow / Vertex AI)

Processing engine (Spark)

Kubeflow / TFX / Ray




Simple Answer to Your Question






From Blogger iPhone client

Vertex ai Types of AI prediction

Here are the main types of predictions in Google Vertex AI, based on how models are deployed and used:





1) Online Predictions (Real-time)



Used when you need an immediate response from the model.


Best for:


  • Chatbots
  • Recommendation systems
  • Fraud detection
  • Real-time personalization



How it works:


  • You send a request → Vertex AI returns a prediction instantly (milliseconds).



Example use case:

You enter text into a chatbot, and it responds immediately.





2) Batch Predictions (Offline)



Used when you have a large dataset and don’t need instant results.


Best for:


  • Scoring large datasets
  • Monthly/weekly analytics
  • Data processing jobs



How it works:


  • You upload a file (e.g., CSV in Cloud Storage)
  • Vertex AI processes it in bulk
  • You get results as another file



Example use case:

Predict churn probability for 1 million customers overnight.





3) Streaming Predictions



Used when predictions must be made continuously on incoming data.


Best for:


  • IoT (Internet of Things)
  • Real-time event processing
  • Live data feeds



How it works:


  • Data flows in (e.g., from Pub/Sub)
  • Vertex AI makes predictions in real-time



Example use case:

Predict equipment failure from live sensor data.





4) AutoML Predictions



Predictions from models trained using Vertex AI AutoML (no coding required).


Types include:


  • AutoML Tables
  • AutoML Vision
  • AutoML Text
  • AutoML Video



Best for:

Business users or teams without deep ML expertise.





5) Custom Model Predictions



Predictions from models you train yourself (TensorFlow, PyTorch, Scikit-learn, etc.).


Best for:


  • Advanced ML teams
  • Research or complex use cases
  • Highly customized AI models



You deploy your model to Vertex AI and then call it for predictions.





6) Foundation Model Predictions (Generative AI)



Using Google’s prebuilt models like:


  • Gemini (text, chat, multimodal)
  • Imagen (image generation)
  • Codey (code generation)



Examples:


  • Generate text
  • Summarize documents
  • Create images
  • Answer questions






Simple Summary Table


Prediction Type

Use Case

Response Time

Online

Chatbots, real-time apps

Very fast

Batch

Large datasets

Slow

Streaming

Live data

Continuous

AutoML

No-code ML

Varies

Custom Model

Advanced ML

Varies

Foundation Model

Generative AI

Fast

From Blogger iPhone client

Power BI workout challenge

https://workout-wednesday.com/power-bi-challenges/

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