Examining Amazon Customer Reviews using PySpark and AWS: A Data Lake Approach

In the contemporary world, big data analysis with PySpark and AWS has become quite handy. Large multinational companies, including Walmart, trivago, and countless others are using big data. And with the assistance of AWS, which offers a variety of services like glue, Athena, S3, etc., becoming extremely beneficial for cloud storage purposes and also for creating data migration pipelines. Any RDBMS database server might send tens of gigabytes of data to an Amazon S3 bucket using the ETL architecture. Our primary goal in this project is to use PySpark to construct an ETL pipeline for data migration. Amazon wireless devices review analysis, Amazon watches review analysis, Amazon books review analysis, Amazon shoes review analysis, and Amazon musical instruments review analysis are the five separate datasets on which we conducted our analysis. The datasets considered for the analysis are Amazon watch reviews, book reviews, shoe reviews, wireless device reviews and musical instrument reviews . The goal of the project is to obtain the ratio of the number of trustworthy, real reviews to untrusted reviews and a comparison in done in the form of graphs.