Analysis of cumulative revenue using Palantir Foundry Function

Question

Analysis of cumulative revenue using Palantir Foundry Function

I am in need of creating a function that can transform raw data into target data. The raw data consists of a table with columns for customer, product, and revenue, while the target data includes customer, revenue, and cumulative revenue.

customer	product	revenue
Customer A	Product 1	EUR 10
Customer A	Product 2	EUR 10
Customer B	Product 1	EUR 5
Customer B	Product 2	EUR 2
Customer C	Product 1	EUR 5

customer	revenue	cumulative revenue
Customer A	EUR 20	EUR 20
Customer B	EUR 7	EUR 27
Customer C	EUR 5	EUR 32

In PySpark, I have written code to achieve this transformation. However, I am not familiar with writing this type of function in TypeScript for front-end calculations "on the fly". Please see below for the PySpark code:

from pyspark.sql import functions as F, window as W
window = W.Window.partitionBy(F.col("helper")).orderBy(F.col("net_revenue").desc())
df = ( df .groupby("customer") .agg( F.sum("net_revenue").alias("net_revenue") ) .withColumn('helper', F.lit(1)) .withColumn( "cumulative_revenue", F.sum("net_revenue").over(window) ) )

Could you provide guidance on how to translate this functionality to TypeScript for front-end implementation?

typescript palantir-foundry

Answer 1

Answer №1

Unfortunately, the current functions do not provide a straightforward solution for this particular workflow. Utilizing the .groupBy(), .sum(), and .segmentBy() functions will not give you the desired outcome.

Furthermore, we have intentionally restricted the number of grouped results for a string (such as customer) to 1000.

Your best course of action would involve iterating through each row of the raw data, establishing a map that tracks the customer along with its revenue and cumulative revenue.

Once you have accomplished that (assuming I grasp your intentions correctly), you can update the respective object's revenue values by using something similar to data.revenue = newRevenue.

To offer you insight into the implementation of this process, here is a snippet of code:

interface CustomerRevenueData {
    revenue: Integer;
    cumulativeRevenue: Integer;
}

@Function()
public cumulativeRevenueAnalysis(): void {
    const data = Objects().search().initialDataObject().all();

    // The string key represents the customer name
    const customRevenueData = new FunctionsMap<string, CustomerRevenueData>();

   data.forEach(dataPoint => {
       // Implement your logic here to adjust the revenue/cumulative revenue for each customer discovered
   })
}

Answer 2

Unfortunately, the current functions do not provide a straightforward solution for this particular workflow. Utilizing the .groupBy(), .sum(), and .segmentBy() functions will not give you the desired outcome.

Furthermore, we have intentionally restricted the number of grouped results for a string (such as customer) to 1000.

Your best course of action would involve iterating through each row of the raw data, establishing a map that tracks the customer along with its revenue and cumulative revenue.

Once you have accomplished that (assuming I grasp your intentions correctly), you can update the respective object's revenue values by using something similar to data.revenue = newRevenue.

To offer you insight into the implementation of this process, here is a snippet of code:

interface CustomerRevenueData {
    revenue: Integer;
    cumulativeRevenue: Integer;
}

@Function()
public cumulativeRevenueAnalysis(): void {
    const data = Objects().search().initialDataObject().all();

    // The string key represents the customer name
    const customRevenueData = new FunctionsMap<string, CustomerRevenueData>();

   data.forEach(dataPoint => {
       // Implement your logic here to adjust the revenue/cumulative revenue for each customer discovered
   })
}

Analysis of cumulative revenue using Palantir Foundry Function

Answer №1

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