Scammers just pulled off a $16 million crypto rug pull using hijacked AI agent accounts. Meanwhile, autonomous agents are trading "weaponised skills" on dark markets, including reverse shells disguised as innocent plugins. And some are storing your API keys in plaintext Markdown files. If you're running autonomous agents in your data pipeline, watch this first:

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?