Analyze the problem statement for the website product recommendations use case and learn the goals to achieve while architecting the solution.
- [Instructor] We now look at website product recommendations, a very popular real-time big data use case. Almost every ecommerce website today recommends products to website visitors based on their interest and browsing patterns. This is a similar use case. Here is the problem for you to solve. Your business wants to recommend products to its users while they are browsing its website.
The recommendation should first be based on the product that is currently being viewed by the user. It should also consider the browsing history for the current user session context. This context includes the previous pages the user navigated and the previous products the user viewed. The recommendation should be a union of both these elements. The task for you is to architect a real-time recommendation system that would generate a list of products to recommend to the user for the current browsing context.
The goals for this solution are as follows. The solution should be able to recommend in real-time within a matter of a few seconds. The recommendation should come up before the user loses interest in the product being viewed. The solution should be context specific, the current product being viewed and the browsing history in the current user session. Both information are available as part of the clickstream events generated by the browser.
The solution should be scalable to support thousands of online users simultaneously. It should be able to provide a response time of a few seconds for that scale.
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