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TL;DR We built a serverless pipeline that ships FSx for ONTAP audit logs to Honeycomb, where its high-cardinality query engine turns file access data into actionable insights. Two delivery paths verified: [Path A: Direct] FSx for ONTAP → S3 Access Point → EventBridge Scheduler → Lambda → Honeycomb Events Batch API [Path B: OTel Collector] FSx for ONTAP → S3 Access Point → EventBridge Scheduler → Lambda → OTel Collector → OTLP → Honeycomb Why Honeycomb for file access logs? Because file access data is inherently high-cardinality : thousands of users × millions of file paths × dozens of operations × multiple SVMs. Traditional log tools force you to pre-aggregate or sample. Honeycomb lets you query the raw events at full resolution. ┌──────────────────────────────────────────────────────┐ │ Honeycomb Query Engine │ │ │ │ "Show me which users accessed /vol/finance/* │ │ between 2am-4am last Tuesday" │ │ │ │ → BubbleUp: auto-detect anomalous dimensions │ │ → Heatmap: visualize access density over time │ │ → GROUP BY user, path, operation — no pre-indexing │ │ │ │ 20M events/month FREE │ └──────────────────────────────────────────────────────┘ This is Part 10 of the Serverless Observability for FSx for ONTAP series. Why Honeycomb for File Access Logs? Most observability tools index a fixed set of fields. When you have high-cardinality dimensions — like file paths ( /vol/data/project-alpha/2026/Q1/report-final-v3.docx ) or Active Directory usernames — you hit index bloat, slow queries, or forced sampling. Honeycomb's columnar storage handles this natively: Capability Traditional Logs Honeycomb Query by arbitrary field Pre-index or full scan Instant (columnar) GROUP BY high-cardinality field Expensive / limited Native BubbleUp (anomaly detection) Manual investigation Semi-automatic (select time range, BubbleUp identifies differing dimensions) Heatmap visualization Requires pre-aggregation Raw events For FSx for ONTAP audit logs, this means you can ask questions like: "Which

Loryn Brantz created The Good Advice Cupcake for BuzzFeed years ago. The company licensed the character for a new Amazon series—made with AI—without her consent.

While Blue Origin investigates the root cause behind last night's spectacular explosion of its New Glenn rocket, it's already clear that this will be a major setback for NASA's Moon base plans and Amazon's fledgling Leo space internet constellation. The incident occurred at about 9pm at Blue Origin's Florida launch site during a hot-fire test, […]

Amazon is turning a corner with its launch providers, but ULA's Vulcan remains grounded.

If you're interviewing at Amazon this year, you've probably read that you need to "prepare STAR stories." What most guides don't tell you is exactly how Amazon uses STAR differently from every other company — and what interviewers are silently scoring you against while you talk. Here's the complete 2026 breakdown: the cheat sheet, the full question bank, scored example answers, and the four mistakes that get candidates rejected even when their stories are genuinely impressive. Why Amazon STAR Is Different Amazon evaluates every behavioral answer against its 16 Leadership Principles. This isn't just culture marketing — interviewers are trained to map your stories to specific LPs and give them discrete scores. A Bar Raiser isn't just listening; they're running a rubric. The STAR formula at Amazon has specific time allocations that most candidates ignore: Situation (10%): Set the context in 20–30 seconds max Task (10%): What was specifically your responsibility Action (50%): What you did — not your team, not your manager Result (30%): Quantified outcomes only That weighting is the whole game. Most candidates spend 60% of their answer on Situation and Task, then rush through Action and Result — which is exactly backwards from what gets high scores. The "I" Rule: The Single Biggest Reason Candidates Fail Bar Raisers flag one thing more than any other: candidates who say "we" during the Action phase. Weak answer: "We decided to refactor the codebase, and we deployed a caching layer to fix the latency issue." Strong answer: "I identified the bottleneck using distributed tracing. I proposed the Redis caching layer to my tech lead and personally implemented the proof-of-concept over a weekend before bringing it to the team." Amazon hires individuals. If you can't cleanly separate your contribution from the group's work, interviewers have no signal on whether you were the driver or just along for the ride. Every sentence in your Action phase should start with "I." 30 Amazon S

The tech giant says a breakthrough in data-center networking has dramatically accelerated the flow of information through its massive cloud infrastructure.

Amazon is moving packages in Japan on the Shinkansen as part of its efforts to have neutral carbon emissions.

Snowflake has signed a new, enormous five-year deal with Amazon to secure chips for AI usage. Nvidia is once again being put on notice.

Amazon Now is coming to "dozens" of locations in 2026.

This is the upgraded smart assistant's debut on a non-Amazon device.

It's an expansion of the mobile app's "Hear the Highlights" feature.