Mleopol1 / Amazon-Customer-Reviews-ETL

The project involves performing the ETL process on Amazon product review datasets by extracting the data, transforming it into different Spark DataFrames using a provided schema, and uploading the DataFrames into an RDS instance.

Amazon Customer Reviews ETL

In this repository, ETL (Extract, Transform, and Load) is performed on two Amazon customer review datasets. The goal of this project is to perform the ETL process completely in the cloud and upload a DataFrame to an RDS instance.

Datasets

Two datasets from Amazon Reviews are used:

• Amazon Video Games Reviews
  • Used in the "part_one_video_games.ipynb" file
• Amazon Musical Instruments Reviews
  • Used in the "part_one_musical_instruments.ipynb" file

Extract

To begin, the datasets are imported using the correct header and sep parameters. The number of rows in each dataset is then counted.

Transform

For each dataset, the schema.sql file located in the Resources folder is used to create the following four DataFrames:

• review_id_df with the appropriate columns and data types.
• products_df that drops the duplicates in the product_id and product_title columns.
• customers_df that groups the data on the customer_id by the number of times a customer reviewed a product.
• vine_df that has the review_id, star_rating, helpful_votes, total_votes, and vine columns.

Load

Finally, each DataFrame is exported into the RDS instance to create four tables for each dataset.

References

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