Learn about some key design issues and recommendations for the individual components within the log accumulation use case.
- [Narrator] Let us now deep dive…into designing key elements…for the server log accumulation pipeline.…We first start with Flume setup…on individual web servers.…Flume is totally driven by configuration.…We will configure Flume to use a file source.…We also set up the Flume agent to archive files,…once they are pushed out,…the archive can be deleted after two weeks.…We need a custom script to do that crooning.…
The Flume agent will use memory as the Flume channel.…This means that there is no archiving of data…while the data is in transit.…That is fine since we are archiving source files anyway…and can be reacquired when required.…The agent will push the data into the Avro server.…The Avro server can be hosted by the Flume consolidator.…We now look at the consolidator…which is technically another instance of the Flume agent.…
The consolidator will host an Avro server…through which it will receive events…from all the web server agents.…The server has a port configured for this purpose.…It uses a file channel,…the file channel will archive the local files,…
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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