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To effectively merge two datasets (customers and sales) using MapReduce and identify the top 10 performers, we can break down the process into a series of steps involving mappers and reducers. Here is a detailed explanation of the solution:
(sale_id, customer_id)
.(customer_id, 1)
. This indicates that each sale is associated with a particular customer, contributing a count of 1.(customer_id, 1)
.customer_id
and sum the counts.(customer_id, num_sales)
, where num_sales
is the total sales per customer.(customer_id, num_sales)
.TreeMap
or priority queue to maintain a sorted list of customers based on num_sales
in descending order.(customer_id, num_sales)
.This approach leverages the power of MapReduce to efficiently process and analyze large datasets, identifying top performers in a structured and scalable manner.