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s3-cost-analysis's Introduction

s3-cost-analysis

STEPS

  1. Use the RA role to get access keys via auth and then run the crawling using the bucket_list() function for the following buckets:
    • r-f-d
    • long-term storage bucket (with filter: early*/)
  2. After that, use the code in main.py to process the Pandas dataframe and calculate the cost, make sure to modify the column names to align them with the actual dataframe, and then finally produce:
    • The accumulated cost year-over-year for the years 2021, 2022, 2023, 2024 (YTD)
    • The accumulated cost for each month
    • Total storage volume per year in TB

Note

  • This calculation is a rough estimate that does not take into the effect of Intelligent-Tiering solutions, we're only using the basic pricing tiers, therefore the estimation would be inflated (the actual cost would be less than these numbers)
  • Reuse this code for further processing use cases

File

  1. simulation.ipynb
    • Contains code on how to create a simulated dataframe that contains some columns
    • file_path, file_size, file_timestamp
  2. main.py
    • With all the relevant functions that helps analyze and plot the data
    • file_path = "s3_inventory.csv"
main(file_path)

s3-cost-analysis's People

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