Get an outline of a blueprint for the website product recommendations use case by identifying individual modules, their responsibilities, and interactions with each other.
- [Instructor] Let us now outline…the architecture for the product recommendations use case.…Let us first review some key points…related to the compound recommendations…we need to do for this use case.…First, the recommendations we need to provide…for the product being viewed…is static or standard.…It means that this list does not change between users…and it changes slowly over time…for a given product.…
It can be predicted on a weekly or monthly basis.…Second, the recommendation based on the clickstream…generated in real time by user actions is dynamic.…The recommendations will change rapidly…as the user navigates between various web pages…in an e-commerce website.…The solution should combine…the static product-based recommendations…and the dynamic clickstream-based recommendations…to provide a consolidated list.…
Now, let us build the solution outline.…We have our data science team…working on past data on user behavior…and coming up with recommendations by product.…The product buy product recommendation…is stored in a product recommendation database.…
There is no coding involved. Instead you will see how big data tools can help solve some of the most complex challenges for businesses that generate, store, and analyze large amounts of data. The use cases are drawn from a variety of industries, including ecommerce and IT. Instructor Kumaran Ponnambalam shows how to analyze a problem, draw an architectural outline, choose the right technologies, and finalize the solution. After each use case, he reviews related best practices for real-time streaming, predictive analytics, parallel processing, and pipeline management. Each lesson is rich in practical techniques and insights from a developer who has experienced the benefits and shortcomings of these technologies firsthand.
- Components of a big data application
- Big data app development strategies
- Use cases: fraud detection and product recommendations
- Technology options
- Designing solutions
- Best practices