Before touching any tools, it helps to know what makes a dataset big in the first place. The usual test is simple: if the data is too large, too fast, or too messy to store and process on a single computer with traditional methods, you are dealing with a big data problem.
You'll start with the five Vs (volume, velocity, variety, veracity, and value) and see where big data comes from and how companies use it. Then you'll compare batch and stream processing, the two ways data gets processed, and follow a typical data pipeline from the source to the people who use it.
This module sets the vocabulary for the rest of the course. Once you've finished it, move on to Programming for Data.
What counts as big data, the five Vs, why data is split across clusters of computers, and the standard ingest, store, and process workflow.
Where big data comes from and how it's used in everyday life, from ads and maps to Netflix recommendations and smart cities.
The difference between batch and stream processing, with real examples of when each one is the right choice.
How data moves from its sources to where it's used, and how batch and streaming pipelines fit into that picture.