Learn about some best practices while building data processing modules within the big data architecture.
- [Instructor] Let us now review some…of the best practices for big data processing.…We start off with some general guidelines.…Plan, architect, and design…for parallelism as much as possible.…Single point operations like reduce operations,…choke a pipeline, so avoid them.…If you can't, optimize them.…Design for reprocessing of data.…It should be possible to go back in time…and reprocess data if required.…
Application logic should allow for the same.…When computing summaries, choose correctly…between batch and incremental summaries.…Batch summaries take more resources and time,…but it allows for reprocessing.…Incremental summaries are quick,…but reprocessing would be a big challenge.…Filter early and often in the data processing cycle.…Less data results in less processing.…Eliminate unwanted data pretty quickly.…
Monitor the data processing pipeline regularly…for choke points and adjust your architecture…to minimize or eliminate them.…Let us now discuss the difference between…real time and historical analytics.…Real time analytics is one where the data…
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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