In your own words describe what ACID means in the context of Data Engineering?

Make a case for this common-sense axiom discussed in this chapter: "Always prioritise requirements over building something cool."? Which of the Greenfield or Brownfield scenarios is it more applicable to?

What is a "Data Swamp" a bad thing? What are the risks and common challenges with Data Lakes?

What is "Caveat Emptor" and why is it important in choosing Data Technology platforms?

Share one aspect of "Good" Data Architecture that you were not familiar with and why it got you thinking about it? 🤔