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BeamPlayArena
Batch LabMedium

Lab: Inventory Analytics

Estimated time: 30 mins

Who This Lab Is For

Intermediate developers looking to master advanced data routing, alerts, and side-output tag collections.

What You Will Learn

  • How to split a single pipeline flow into multiple branches.
  • How to output elements to tagged side-outputs using TaggedOutput.
  • How to aggregate global inventories alongside logging alerts in parallel.

1. Business Scenario

Calculate total stock valuation and identify low stock items.

2. Input Dataset (\`dataset.csv\`)

Save the following raw rows locally as \`dataset.csv\` to test your pipeline:

text
item_code,name,stock_level,unit_cost
item1,Widget A,150,2.50
item2,Widget B,40,12.00
item3,Widget C,8,45.00
item4,Widget D,120,1.25
item5,Widget E,5,80.00

3. Starter Code Skeleton

Create a local file named \`starter.py\` and copy the following skeleton. Complete the missing transformations:

python
# starter.py - Inventory Analytics
import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions

def run_pipeline():
    options = PipelineOptions()
    with beam.Pipeline(options=options) as p:
        # TODO: Calculate values
        # TODO: Separate low stock items using dynamic routing
        # TODO: Output inventory reports
        pass

if __name__ == "__main__":
    run_pipeline()

4. Lab Requirements

  • Calculate total valuation per item (stock_level * unit_cost).
  • Identify and tag low stock items (stock_level < 15) using Side Outputs.
  • Calculate total inventory cost valuation globally.

5. Step-by-Step Guide & Solution

Solution for Inventory Analytics

Click below to reveal the complete, runnable Python SDK implementation solution and the step-by-step walkthrough to complete the lab.

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