Large data volume

Hi Soumeli ,

You are dealing with a "Big Data" scenario where the standard rules of Power BI Desktop no longer apply. 500 million rows will almost certainly crash a standard Import model (especially on Pro licenses) and will be painfully slow in pure DirectQuery mode without optimization.

Building on the excellent advice about Aggregations, here are two specific architectural patterns you should investigate to make this usable:

1. The "Hybrid Table" Approach (Incremental Refresh) If you are on a Premium (or PPU) capacity, you can use Hybrid Tables.

• How it works: You configure Incremental Refresh to keep the last 2-3 months of data in Import Mode (for blazing fast speed on recent data) and leave the rest of the 500M rows in DirectQuery Mode (archived).

• The Benefit: To the user, it looks like one single seamless table. They can scroll back 10 years, and Power BI automatically switches the query method behind the scenes. They get speed where it matters (recent data) and access where it matters (history).

2. Fabric "Direct Lake" (The Modern Solution) If your organization has enabled Microsoft Fabric, this is the silver bullet for your problem.

• How it works: You load your 500M rows into a Delta Table in OneLake. You connect Power BI using Direct Lake mode.

• The Benefit: It gives you the performance of Import mode without having to copy the data into memory. It can handle hundreds of millions of rows because it loads columns into memory on-demand from the OneLake storage.

Critical "User Management" Advice Even with these tools, you must manage user expectations.

• Users typically say "I want raw data," but they usually mean "I want to see the total, and then drill down."

• Trying to render 500M individual points on a line chart will just show a solid block of color (and likely time out).

• Ensure your report defaults to a high-level view (Year/Month) and only allows the "Table View" of raw records when the user has filtered down to a manageable subset (e.g., using the "Drillthrough" feature).

Start with Aggregations as suggested above, it is the most robust way to solve this today without requiring new licenses immediately.

If my response resolved your query, kindly mark it as the Accepted Solution to assist others. Additionally, I would be grateful for a 'Kudos' if you found my response helpful.
This response was assisted by AI for translation and formatting purposes.

Hi Soumeli 

Power BI is not designed to visualize hundreds of millions of raw rows directly.
At this scale, the solution is not loading everything, but changing the architecture.

Recommended approach:

Pre-aggregate data in the source (DWH / SQL / Lake)

Use aggregated tables for reporting

Combine Import + DirectQuery with Aggregations

Use Incremental Refresh if Import mode is involved

“All historical data” does not mean “all rows shown at once”.
Power BI visuals work on aggregations, not raw data exploration.

If this post helps, then please consider Accepting it as the solution to help the other members find it more quickly

Power BI cannot handle 500M+ growing rows “as-is” in Import mode. With Pro, semantic models are limited to ~1 GB compressed, so this volume will exceed limits very quickly. DirectQuery alone also won’t solve it, queries will be slow and the user experience poor once people start slicing or drilling.

The practical pattern is:

• Keep raw historical data outside Power BI (SQL, Databricks, Fabric, etc.).

• Expose pre-aggregated tables (daily / monthly / entity level) to Power BI.

• Use incremental refresh or composite models so recent data is detailed and older data stays aggregated.
  Users still “see all history”, but not at raw-row level unless they explicitly drill.

If this must keep growing indefinitely, the realistic long-term solution is a warehouse / Fabric Lakehouse + Power BI semantic model. Power BI becomes the semantic and visualization layer, not the place where 500M raw rows live.

Hi Soumeli 

Pattern 1 - Import + Incremental Refresh

Even if users want all history, refresh does not need to reload all history.
How?
Keep historical partitions static
Refresh only recent data (e.g., last 7–30 days)

Key point
Incremental Refresh is about refresh cost, not filtering user access.
Required for large datasets
Works with full historical reporting

What not to do

Import raw transactional data at full grain
Rely on slicers to “limit” memory usage
Expect Power BI Pro to behave like a database
Recalculate heavy logic in DAX instead of ETL

Recommended Architecture by License
Power BI Pro
Import aggregated fact
Incremental refresh
Drill-through to limited detailMax practical size - 10–30M rows

Power BI Premium/PPU
Large semantic model
Aggregations
Composite models
Can scale to 100M+ rows

With Fabric / Data Warehouse
Raw Data (500M+)
→ Warehouse / Lakehouse
→ Aggregated Fact Tables
→ Power BI Semantic Model

Power BI is designed to be a semantic and analytical layer.

Finally
For 500M+ and growing
Pre-aggregate data
Using incremental refresh
Exposing full history via aggregates
Allowing  drill-through for detail
Planning for Premium/Fabric if growth continues

Please mark it as a solution with headup if this helps you. Thank You!

Hi Soumeli ,

Thank you krishnakanth240  for the response provided!

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Thank you for your understanding.