Learn about some best practices while building data Acquisition modules within the big data architecture.
- [Lecturer] Let us review some of the best practices…for big data acquisition.…Acquire each record on once.…Have checks and balances to ensure that…duplicates don't happen.…The source and the data pump should…provide for these features.…Failures always happen that break the pipeline.…Build the ability to re-acquire…and re-transmit data just in case.…Alternatively build redundancy capabilities…to ensure that individual node failures…do not impact the entire system.…
As much as possible denormalize the data at source…or as early as possible.…Big data stores are good at storing…text data but bad at joining data.…There are going to be volume spikes in the data.…This could lead to choking at the source,…data pump or the destination.…Provide for buffering and parallel crossing…to handle such loads.…Choose products that provide these out of the box.…
Finally consider data security and privacy…and ensure that the pipelines…ar architected to meet required corporate standards…for security and privacy.…
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)
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.