Common Issues while using Data lake as source for Power BI

Hello,

I’m not completely sure, but yes, performance can become an issue when using ADLS or S3 directly as a Power BI source, especially with large files, too many small files, or bad partitioning. Query folding also does not always work well, which can slow refreshes a lot

I would prefer Parquet instead of CSV, partitioning data by date, adding incremental refresh, or putting a layer like Synapse or Databricks so queries run smoother

Best regards,
Daniele

Hey,

Completely agree with DanieleUgoCopp. Just to summarize in pointers:

• Query Folding = ADLS/S3 native connectors can't push filters back to the source, so Power BI pulls the entire dataset (no query folding). Fix: route through Lakehouse or Dataflow Gen2 to enable folding.
• File Format = CSV and JSON are slow to scan for analytics. Fix: Parquet or Delta format are columnar, compressed and faster.
• Indexing or Partitioning = Data lakes have no indexes, so Power BI can't skip irrelevant data. Fix: partition files by date/region and query through Synapse/Athena to leverage partition pruning.
• DirectQuery vs Import mode = In case of DirectQuery, every visual interaction fires a new query to the lake. Fix: prefer Import mode wherever possible; only use DirectQuery if real-time data is a hard requirement.
• Full vs Incrmental Refresh = Without incremental refresh, the entire lake is reloaded each time. Fix: set up incremental refresh using RangeStart/RangeEnd parameters.
• Network Latency = Large data volumes travelling from cloud storage slow everything down. Fix: pre-aggregate heavy tables in a gold layer so Power BI only pulls what it needs.

Best,

Harshit