Finesse the earlier architecture blueprint for the customer analytics use case with technology options and finer details, to arrive at the final architecture.
- [Instructor] Let us now lay out the architecture…for our customer analytics use case.…We have our Customer 360 store.…We split the store in the previous use case into two stores;…the customer summary stored in Cassandra,…and the customer transaction details…stored in Elasticsearch.…We will use Kibana as the visualization tool…for this use case.…We will build a custom Rest API module in Java.…
We can use Spring framework and its components…to build out this module easily.…Spring allows us to easily integrate…other requirements like security…and scalability into our module…without significant additional effort.…
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 data acquisition, transport, processing, storage, and service. 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: archiving audit logs and performing customer analytics
- Technology options
- Designing solutions
- Best practices
Skill Level Advanced
Big Data Foundations: Program Managementwith Alan Simon1h 11m Intermediate
1. Intro to Big Data Applications
2. Use Case 1: Data Warehouse (DW)
3. Use Case 2: Log Accumulation (LA)
4. Use Case 3: IT Operations Analytics (OA)
5. Use Case 4: Customer 360 (C360)
6. Use Case 5: Customer Analytics (CA)
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