Learn about the big data characteristics that provide challenges to architects while designing big data applications.
- [Instructor] By now you have already heard about big data, but for the sake of this course, let's review the key elements. The first characteristic of big data is Volume. We are talking about terabytes and megabytes of data. Volume that cannot be stored and handled with just a few servers. Second, is Velocity. That's the speed at which data is created, moved and processed.
Big data is today generated by millions of users and devices for any given application. The movement and processing of data should keep up with the velocity of generation in order for this data to be useful. The third distinct characteristic of big data is Variety. Data is no longer just numbers. We have text, audio, and video. These data forms take up more resources to store and process.
They also need to be dissected and analyzed to generate meaningful information. And finally, there is Variability. Big data gives you no guarantees about steady speed. There are spikes and valleys in its generation. The movement and processing pipelines need to provide for these variations, and should not choke. Given these characteristics, how do we architect applications that effectively and efficiently handle big data? That is the question I will answer throughout this course.
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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