What are the limitations of using spreadsheets

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Reference no: EM133737101

Case Scenario: You're a data analyst tasked with analyzing a large sales dataset to identify top-selling products across different regions.

Spreadsheets as a (Very) Simple Distributed System:

Data Partitioning: Imagine a large sales dataset that wouldn't fit comfortably in a single spreadsheet. Divide the data by region (e.g., North, South, East, West) and create separate spreadsheets for each region. This simulates how data gets partitioned across multiple nodes in a distributed system.

Parallel Processing (Manual): Open all the regional spreadsheets simultaneously. Now, imagine you have a team of analysts (you can play all the roles!). Each analyst would work on a separate regional spreadsheet, calculating sales totals or other relevant metrics for their assigned region. This mimics parallel processing where tasks are distributed across multiple processing units.

Aggregation and Insights: Once each analyst finishes working on their regional data, come back together and combine the results from all spreadsheets (e.g., manually copy and paste into a master sheet). This simulates how partial results from distributed processing are aggregated to generate overall insights.

Discussion Points:

How did dividing the data and working in parallel help in this scenario?

What are the limitations of using spreadsheets for large-scale data analysis?
How do distributed computing frameworks like Hadoop or Spark overcome these limitations?

Can you think of other ways to partition data besides using regions? (e.g., product category, customer type)
Further Exploration:

Research real-world examples of big data analytics and the role of distributed computing frameworks.
Explore online tutorials or simulations that demonstrate how Hadoop or Spark work at a basic level.

Reference no: EM133737101

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