Learn about the various modules of a big data architectures, as well as their responsibilities and challenges.
- [Instructor] Traditional software applications…typically dealt with user interfaces.…That means they depended upon a user to enter a lot of data.…Big data does not.…Rather, it focuses on collecting massive amounts of data,…not just from clients, but also endpoints…and other distributed sources.…The objective then is to efficiently move,…process, and analyze data to deliver knowledge and actions.…
Big data architectures typically focus…on building pipelines.…The various stages in a data pipeline are,…acquisition, transport, storage, processing, and servicing.…Let us review the stages and their responsibilities…in a big data architecture.…The first stage in a big data pipeline is data acquisition.…Data acquisition modules focus on the sources of data.…
The engineering challenges in this stage…involve answering the following questions.…What is the format of source data?…Data can be raw bytes, text, files, or databases.…What is the best way to acquire them?…Which interfaces are available to acquire this data?…This can be standard protocols like JDBC,…
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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