Learn about some best practices while building data service modules within the big data architecture.
- [Instructor] In this video,…we look at the Best Practices for Data Service.…We start with Data Visualization.…What do we look for in Data Visualization Tools?…First, the tools should be easy to use…without requiring significant training…or programming skills.…It should fit the use case.…Some tools are good with numbers and tables.…Others work well with raw data or text.…The tools should provide for creating…flexible reports and dashboards.…
It should provide excellent graphical capabilities.…It should be possible to create use-case-driven,…and user-driven dashboards,…by combining multiple visualizations…and data points.…Scheduling of visualizations and dashboards…is a key capability to send out reports…at predefined intervals to other consumers.…Finally, the tools should have excellent…compatibility with the back end databases you use.…This is important because…the tool-to-database link impacts performance significantly.…
An optimized connection is required…for good response times.…We now look at some of the Best Practices…
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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