Ecommerce analytics is the process of collecting data from all of the sources that affect a certain shop. Analysts can then utilize this information to deduce changes in customer behavior and online shopping patterns. Ecommerce analytics spans the whole customer journey, from discovery through acquisition, conversion, and eventually retention and support.

In this project, we will use an eCommerce dataset to simulate the logs of user purchases, product views, cart history, and the user’s journey on the online platform to create two analytical pipelines, Batch and Real-time. The Batch processing will involve data ingestion, Lake House architecture, processing, visualization using Amazon Kinesis, Glue, S3, and QuickSight to draw insights regarding the following:

Unique visitors per day

During a certain time, the users add products to their carts but don’t buy them

Top categories per hour or weekday (i.e. to promote discounts based on trends)

To know which brands need more marketing

The Real-time channel involves detecting Distributed denial of service (DDoS) and Bot attacks using AWS Lambda, DynamoDB, CloudWatch, and AWS SNS.