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World-class Power BI dashboards, generated from any data source.

Point pbigen at a table. It reads the schema, reasons about the shape of the data, and writes a clean, ready-to-open Power BI project — deterministically, or refined by any LLM. Free, local, open source.

Get started Why pbigen? Browse the recipes

16 data sources · deterministic or any LLM · MIT · no cloud capacity

Install¶

pip install pbigen                 # core (dependency-light)
pip install "pbigen[bigquery]"     # + one source driver: bigquery, snowflake, lakehouse, …
pip install "pbigen[llm]"          # + optional LLM-refined design

60 seconds, no cloud account¶

pip install "pbigen[lakehouse]" pyarrow
python -c "import pyarrow as pa,pyarrow.parquet as pq,random,datetime as d; r=random.Random(1); \
pq.write_table(pa.table({'order_date':[d.date(2024,1,1)+d.timedelta(days=r.randint(0,540)) for _ in range(500)],\
'region':[r.choice(['North','South','East','West']) for _ in range(500)],\
'status':[r.choice(['New','Shipped','Returned']) for _ in range(500)],\
'revenue':[round(r.uniform(50,5000),2) for _ in range(500)]}),'orders.parquet')"

pbigen generate --source parquet --set uri=orders.parquet \
  --objective "Sales overview by region and status over time" --theme midnight --out out

Open out/Orders/Orders.pbip in Power BI Desktop (enable the PBIR preview once — see Recipes → Open in Desktop).

What you can do¶

Connect anything BigQuery/BigLake/Omni, Snowflake, Redshift, Synapse/Fabric, Databricks, ClickHouse, Postgres, Athena, a Parquet/Iceberg/Delta lake on GCS/S3/ADLS, or a Cube semantic layer. Sources & auth →
Deterministic or LLM design A reproducible, cardinality-aware engine by default (no key, no network); optionally let any LLM refine it — metadata only. Deterministic vs LLM →
Match any house style Built-in executive themes, bring-your-own theme JSON, a logo, left/right nav — or extract the theme + logo from a shared .pbix and pour your data into that shell. Themes, logo & shell →
Controls that matter Import vs DirectQuery, row_limit sampling for huge tables, and a clean Python API + CLI. Recipes →
Safe & portable Read-only, metadata-only, standards-based PBIP output that validates against Microsoft's schemas and opens in Desktop. Version-control it like code.
Test everything A self-serve verification harness — offline check, per-source, schema validation, and the full matrix. Testing →

Why not just use Copilot or an agentic BI tool?¶

Most AI dashboard tools are cloud services that build inside their own surface and cost per seat or per capacity. pbigen is a small open-source library that produces portable Power BI files you own, on your machine, for free — deterministically if you want, with no data leaving your environment. See the full breakdown: Why pbigen →