Microsoft Fabric OneLake Usage

Microsoft Fabric / OneLake Usage

Great Generator can create CSV or Parquet data that can be used in Microsoft Fabric Lakehouse, OneLake, notebooks, semantic model demos, and Power BI dashboards.

Recommended pattern

1. Generate data locally or in a Spark notebook.
2. Write CSV or Parquet files.
3. Upload or write the files into a Fabric Lakehouse / OneLake path.
4. Build notebooks, SQL analytics endpoints, semantic models, or Power BI reports on top of the generated tables.

Pandas example

from great_generator import generate_domain

sales = generate_domain("ecommerce", scale="small", realism="realistic")

for table, df in sales.items():
    df.to_parquet(f"./fabric_seed/ecommerce/{table}.parquet", index=False)

Spark example

data = generate_domain("banking", engine="spark", scale="medium", realism="realistic")

data["transactions"].write.mode("overwrite").parquet(
    "Files/synthetic/banking/transactions"
)

Good Fabric demo use cases

• Power BI dashboards without production data
• Lakehouse table modeling
• semantic model testing
• medallion architecture workshops
• notebook tutorials
• data quality demos

Great Generator creates synthetic data from templates. Do not mix it with real workplace or customer data when creating public demos.