DATA.md

Data

Raw

• Check data/raw directory for all raw product sales data. Each file is in csv format, and each entry follow below spark schema:

root
 |-- ProductName: string (nullable = true)
 |-- Category: string (nullable = true)
 |-- Price: double (nullable = true)
 |-- Quantity: integer (nullable = true)
 |-- Date: date (nullable = true)

Aggregation

• Check data/analyses/aggregate-overall directory for output data generated when run AggregationOverall.scala analysis. Each file is in csv format, and each entry follow below spark schema:

root
 |-- TotalSalesQuantity: long (nullable = true)
 |-- TotalSalesAmount: double (nullable = true)
 |-- TotalSalesDay: long (nullable = false)

• Check data/analyses/aggregate-sales-per-category directory for output data generated when run AggregationPerCategory.scala analysis. Each file is in csv format, and each entry follow below spark schema:

root
 |-- Category: string (nullable = true)
 |-- TotalSalesQuantity: long (nullable = true)
 |-- TotalSalesAmount: double (nullable = true)
 |-- TotalSalesDay: long (nullable = false)

• Check data/analyses/aggregate-sales-per-date directory for output data generated when run AggregationPerDate.scala. Each file is in csv format, and each entry follow below spark schema:

root
 |-- Date: date (nullable = true)
 |-- TotalSalesQuantity: long (nullable = true)
 |-- TotalSalesAmount: double (nullable = true)

• Check data/analyses/aggregate-sales-per-product directory for output data generated when run AggregationPerProduct.scala. Each file is in csv format, and each entry follow below spark schema:

root
 |-- ProductName: string (nullable = true)
 |-- TotalSalesQuantity: long (nullable = true)
 |-- TotalSalesAmount: double (nullable = true)
 |-- TotalSalesDay: long (nullable = false)
 |-- AverageSellingPrice: double (nullable = true)

Running Total

• Check data/analyses/running-total-overall directory for output data generated when run RunningTotalOverall.scala analysis. Each file is in csv format, and each entry follow below spark schema:

root
 |-- Date: date (nullable = true)
 |-- TotalSalesQuantity: integer (nullable = true)
 |-- TotalSalesAmount: double (nullable = true)
 |-- RunningTotalSalesQuantity: long (nullable = true)
 |-- RunningTotalSalesAmount: double (nullable = true)

• Check data/analyses/running-total-per-category directory for output data generated when run RunningTotalPerCategory.scala analysis. Each file is in csv format, and each entry follow below spark schema:

root
 |-- Date: date (nullable = true)
 |-- TotalSalesQuantity: integer (nullable = true)
 |-- TotalSalesAmount: double (nullable = true)
 |-- RunningTotalSalesQuantity: long (nullable = true)
 |-- RunningTotalSalesAmount: double (nullable = true)

Rolling Average

• Check data/analyses/rolling-average-overall directory for output data generated when run RollingAverageOverall.scala analysis. Each file is in csv format, and each entry follow below spark schema:

root
 |-- Date: date (nullable = true)
 |-- TotalSalesQuantity: integer (nullable = true)
 |-- TotalSalesAmount: double (nullable = true)
 |-- RollingAverageSalesQuantity: double (nullable = true)
 |-- RollingAverageSalesAmount: double (nullable = true)

• Check data/analyses/rolling-average-per-category directory for output data generated when run RollingAveragePerCategory.scala analysis. Each file is in csv format, and each entry follow below spark schema:

root
 |-- Date: date (nullable = true)
 |-- TotalSalesQuantity: integer (nullable = true)
 |-- TotalSalesAmount: double (nullable = true)
 |-- RollingAverageSalesQuantity: double (nullable = true)
 |-- RollingAverageSalesAmount: double (nullable = true)