Distinguish between traditional and big data applications, and identify unique challenges with big data applications.
- [Instructor] The world of IT has built traditional IT…applications for a number of decades,…applications that are number-crunching,…enterprise-built and siloed.…A number of big data students come from the world…of traditional IT.…While moving to big data, it is important to understand…and appreciate the differences between…traditional applications and big data application.…The first difference is data types.…
Traditional applications dealt with numbers…and numbers only.…They collected, stored, computed and displayed numbers.…The processing and storage software used were also…optimized for numbers.…Big data applications deal with text, audio and video.…Except for cases like sensors, numbers are widely collected…in big data.…Big data software and applications should be optimized…to deal with these new data types.…
Another difference is schema.…Creating an upfront, relational schema with well-defined…sizes, constraint and relationships, is the Holy Grail…of traditional applications.…Schema gave structure and control…
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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