Schema normalization vs. messy logs

In this episode, Cliff Crosland, CEO & co-founder of Scanner.dev, shares his candid journey of trying (and initially failing) to build an in-house security data lake to replace an expensive traditional SIEM.

Cliff explains the economic breaking point where scaling a SIEM became "more expensive than the entire budget for the engineering team". He details the technical challenges of moving terabytes of logs to S3 and the painful realization that querying them with Amazon Athena was slow and costly for security use cases .

This episode is a deep dive into the evolution of logging architecture, from SQL-based legacy tools to the modern "messy" data lake that embraces full-text search on unstructured data. We discuss the "data engineering lift" required to build your own, the promise (and limitations) of Amazon Security Lake, and how AI agents are starting to automate detection engineering and schema management.

Guest Socials -⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cliff's Linkedin

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Questions asked:

(00:00) Introduction(02:25) Who is Cliff Crosford?(03:00) Why Teams Are Switching from SIEMs to Data Lakes(06:00) The "Black Hole" of S3 Logs: Cliff's First Failed Data Lake(07:30) The Engineering Lift: Do You Need a Data Engineer to Build a Lake?(11:00) Why Amazon Athena Failed for Security Investigations(14:20) The Danger of Dropping Logs to Save Costs(17:00) Misconceptions About Building Your Own Data Lake(19:00) The Evolution of Logging: From SQL to Full-Text Search(21:30) Is Amazon Security Lake the Answer? (OCSF & Custom Logs)(24:40) The Nightmare of Log Normalization & Custom Schemas(28:00) Why Future Tools Must Embrace "Messy" Logs(29:55) How AI Agents Are Automating Detection Engineering(35:45) Using AI to Monitor Schema Changes at Scale(39:45) Build vs. Buy: Does Your Security Team Need Data Engineers?(43:15) Fun Questions: Physics Simulations & Pumpkin Pie