The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: KeyError
Message: "There is no item named 'DimAccount/QID8816_20170924_35728_0.txt' in the archive"
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
for key, example in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 503, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 350, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/text/text.py", line 69, in _generate_tables
with open(file, encoding=self.config.encoding, errors=self.config.encoding_errors) as f:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/streaming.py", line 73, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 935, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/core.py", line 135, in open
return self.__enter__()
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/core.py", line 103, in __enter__
f = self.fs.open(self.path, mode=mode)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/spec.py", line 1293, in open
f = self._open(
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/implementations/zip.py", line 129, in _open
out = self.zip.open(path, mode.strip("b"), force_zip64=self.force_zip_64)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1608, in open
zinfo = self.getinfo(name)
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1536, in getinfo
raise KeyError(
KeyError: "There is no item named 'DimAccount/QID8816_20170924_35728_0.txt' in the archive"Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Contoso Retail Data Warehouse – Dataset Overview
This dataset represents the Contoso Retail Data Warehouse, a sample analytical model commonly used for demonstrations involving SQL, ETL, BI, data modeling, and analytics. This data is used in the Synapse tutorial
In this distribution, each table is stored as a separate ZIP file. Every ZIP contains one subfolder with raw, pipe-separated text files representing that table’s data.
ContosoRetailDW/
├── DimCustomer.zip
│ └── DimCustomer/
│ ├── QID8828_20170924_35754_1.txt
│ ├── QID8828_20170924_35754_2.txt
│ └── ...
├── FactSales.zip
| └── FactSales/
│ ├── QID8927_20170924_35812_1.txt
└── ...
These files can be imported into SQL databases, Fabric Warehouse/Lakehouse, Synapse, or any analytics platform that supports delimited text ingestion.
📊 Table Overview
Tables are grouped into Dimension and Fact tables.
🟦 Dimension Tables
DimAccount
Chart of accounts used for financial reporting and planning.
DimChannel
Sales channels such as online, in‑store, catalog, and partner routes.
DimCurrency
List of global currencies with ISO codes and attributes.
DimCustomer
Retail customers with demographic and location attributes.
DimDate
Calendar table used for time‑based analytics.
DimEmployee
Employee information including roles, departments, and hierarchy.
DimEntity
Organizational units used for budgeting and planning.
DimGeography
Countries, cities, and regions with geographical details.
DimMachine
Details about IT or production machines tracked by the business.
DimOutage
Outage and downtime classifications.
DimProduct
Products sold by Contoso, including SKU‑level details.
DimProductCategory
High-level product categories.
DimProductSubcategory
Subcategories nested under product categories.
DimPromotion
Marketing promotions and discounts applied during sales.
DimSalesTerritory
Sales regions and territories.
DimScenario
Scenario classifications such as Actual, Budget, Forecast.
DimStore
Retail store locations with size, type, and related attributes.
🟥 Fact Tables
FactExchangeRate
Daily or periodic currency exchange rates.
FactInventory
Inventory levels by product, store, and date.
FactITMachine
Operational metrics for machines in DimMachine.
FactITSLA
Service Level Agreement (SLA) performance metrics for IT assets.
FactOnlineSales
Transaction-level online sales including customer, product, and amounts.
FactSales
Comprehensive sales transactions across all sales channels.
FactSalesQuota
Sales targets per employee, region, or period.
FactStrategyPlan
Strategic financial and operational planning measures.
📥 Usage Notes
- Can be loaded using:
- BULK INSERT / COPY INTO / PolyBase / OPENROWSET
- Azure Fabric, Synapse, Data Factory
- Python, Spark, or other ETL frameworks
- This dataset follows a classic star schema used for retail analytics.
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