Review the technology options available for the mobile couponing use case, compare them, and choose the right technology for the modules.
- [Instructor] Let us look…at the possible technology options…we can use for this use case.…We start off with our coupon recommendation engine.…The engine should look up user preferences…and location services…and decide on the coupons to publish.…We will use Apache Spark for this engine…based on evaluations done in the previous use cases.…Next, we look at real-time stream and coupon queues.…
We will use Apache Kafka for the message queue…based on the selection criteria…we have discussed in the earlier use cases.…We would have also used RabbitMQ…and ActiveMQ since they are comparable in features…but Kafka has better integration into Apache Spark,…our recommendation engine.…Finally, we look at the mobile gateway.…The mobile gateway on one side talks to the mobile network…and on the other side to Kafka.…
We will build a custom mobile gateway service…for the same as standard out-of-the-box open source products…are not available for this gateway.…
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