Explore how to match the data hygiene requirements to the possible methods of transforming data—ETL. Learn about the practicalities of solutions which combine these methods.
- [Instructor] Another concept we need to consider…before we get started working with AWS Analytics,…is data hygiene.…As I mentioned in a previous movie,…in our examples, our data will be clean, pristine,…and easy to understand.…And I'll be honest, and tell you…this does not reflect the real world.…It does allow us to focus on what the services do,…which is important for this course.…But again, I always like to bring my…real world experience into what I share with my students.…So what I find with my customers,…is that very very frequently they need to invest…significant resources in data hygiene,…at the beginning of any new analytics…project that I work with them on.…
So the concepts around this have the letter E in them.…So let's just talk about those.…What is ETL?…Extract, transform, and load.…The idea here, is that to build an analytic solution,…you're going to need to design a process…that's going to retrieve data…out of a number of source systems,…clean or transform the data, preparing it for sending it…into destination systems, and then load it.…
- Explain the difference between files and databases.
- Identify examples of batching, micro-batching, and streaming.
- Prepare helpful data visualizations with QuickSight.
- Recognize the different types of analytics available in AWS.
- Demonstrate how to set up AWS CLI.
- Describe common analytics architecture patterns.
Skill Level Intermediate
Amazon Web Services for Data Sciencewith Lynn Langit3h 56m Intermediate
1. Analytics on AWS
2. Analytic Services
3. AWS Code Tools for Analytics
4. Advanced Analytics
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