Get an outline of a blueprint for the mobile couponing use case by identifying individual modules, their responsibilities, and interactions with each other.
- [Instructor] Let us now outline the solution…for real-time mobile couponing.…Let us first elaborate and understand…how the recommendations should work.…We will use predictive analytics…to identify the types of services…and products the user would be interested in buying.…This uses the history of the coupons…pushed to the user in the past…and whether they actually bought the products or services.…This is batch processing.…
We will track the location of the user…from his or her mobile phone in real time.…This is dynamic processing.…We will choose coupons for the services…that the user would prefer…based on their history…and those that are near his or her current location,…then push these coupons to their phones.…Let us now get to the drawing board.…We have the user history database…containing the past coupon-buying behavior…of the customers.…
Your data science team…would build models to predict…user preferences based on the user's history database.…The preferences would then be stored…in a user preferences 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