Outline a blueprint for the log accumulation use case by identifying individual modules, as well as their responsibilities and interactions with each other.
- [Instructor] Let us now outline the solution…for the server log accumulation pipeline.…We have these individual webservers…where performance logs are collected.…These logs are rolled over as they fill up.…Let's install an agent in this webserver…that would watch for newly-created logs,…acquire these log data,…and publish them into a log transport network.…
We will set up similar agents…on each of these 200 webservers.…Now we create a central consolidator…that would receive the log events…from each of these webservers.…The agents on the webservers…will push the log events to the consolidator.…On the consolidator, we will also have…transformation capabilities…that will transform the log messages…into suitable formats that can be used by systems…later in the pipeline.…
The transform messages will then be sent to…the log archive database we create.…The same message will also be sent…to the Network Management System for its own use.…
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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