Batch LabMedium

Lab: HR Analytics

Estimated time: 30 mins

Who This Lab Is For

Intermediate developers looking to learn department-based statistics and global maximum value tracking.

What You Will Learn

How to calculate mean statistics per key using MeanCombineFn.
How to implement custom comparison logic to query global maximum values.
How to split computations into separate parallel branches from a single source.

1. Business Scenario

Compute average salary by department and identify the highest earner.

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

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

text

emp_id,dept,salary
e1,HR,60000
e2,Engineering,140000
e3,Engineering,110000
e4,Sales,80000
e5,HR,65000

3. Starter Code Skeleton

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

python

# starter.py - HR 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: Compute average salary per department
        # TODO: Identify highest overall salary
        pass

if __name__ == "__main__":
    run_pipeline()

4. Lab Requirements

Parse department and salary attributes.
Calculate the average salary per department.
Find the employee with the highest salary globally using Max combinators.

5. Step-by-Step Guide & Solution

Solution for HR 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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