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"Alexa, can you handle big data?"

Check out my final SQL analysis and report!

Background

In this assignment you will put your ETL skills to the test. Many of Amazon's shoppers depend on product reviews to make a purchase. Amazon makes these datasets publicly available. However, they are quite large and can exceed the capacity of local machines to handle. One dataset alone contains over 1.5 million rows; with over 40 datasets, this can be quite taxing on the average local computer. Your first goal for this assignment will be to perform the ETL process completely in the cloud and upload a DataFrame to an RDS instance. The second goal will be to use PySpark or SQL to perform a statistical analysis of selected data.

There are two levels to this assignment.

  1. Create DataFrames to match production-ready tables from two big Amazon customer review datasets.
  2. Analyze whether reviews from Amazon's Vine program are trustworthy.

Task

Level 1

  • Use the furnished schema to create tables in your RDS database.

  • Create two separate Google Colab notebooks and extract any two datasets from the list at review dataset, one into each notebook.

  • Be sure to handle the header correctly.

  • For each notebook (one dataset per notebook), complete the following:

    • Count the number of records (rows) in the dataset.

    • Transform the dataset to fit the tables in the schema file. Be sure the DataFrames match in data type and in column name.

    • Load the DataFrames that correspond to tables into an RDS instance. Note: This process can take up to 10 minutes for each. Be sure that everything is correct before uploading.

Level 2

In Amazon's Vine program, reviewers receive free products in exchange for reviews.

vine01.png

Amazon has several policies to reduce the bias of its Vine reviews: https://www.amazon.com/gp/vine/help?ie=UTF8.

But are Vine reviews truly trustworthy? Your task is to investigate whether Vine reviews are free of bias. Use either PySpark or—for an extra challenge—SQL to analyze the data.

  • If you choose to use SQL, first use Spark on Colab to extract and transform the data and load it into a SQL table on your RDS account. Perform your analysis with SQL queries on RDS.

  • While there are no hard requirements for the analysis, consider steps you can take to reduce noisy data, e.g., filtering for reviews that meet a certain number of helpful votes, total votes, or both.

  • Submit a summary of your findings and analysis.

Submission

  • Important be sure to clean up all your AWS resources. Consult the AWS cleanup guide and AWS check billing guide as reference.

  • Important: Do not upload notebooks that contain your RDS password and endpoint. Be sure to delete them before making your notebook public!

References

Amazon customer Reviews Dataset. (n.d.). Retrieved April 08, 2021, from: https://s3.amazonaws.com/amazon-reviews-pds/readme.html

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