标签：#amazonfsxfornetappontap

TL;DR We built a serverless Lambda pipeline that ships FSx for ONTAP audit logs to Sumo Logic's JP (Tokyo) region deployment. For Japanese enterprises with data residency requirements under APPI (Act on the Protection of Personal Information), this means audit logs never leave Japan. FSx for ONTAP → S3 Access Point → EventBridge Scheduler → Lambda → Sumo Logic HTTP Source (JP) │ ▼ ┌───────────────────┐ │ Sumo Logic JP │ │ (Tokyo) │ │ │ │ • 500 MB/day FREE │ │ • Data stays in │ │ Japan │ │ • 7-day retention │ │ (free tier) │ └───────────────────┘ Key advantages: 500 MB/day free tier (~15 GB/month) — covers most FSx for ONTAP deployments at zero vendor cost JP region deployment — data residency in Tokyo Simplest auth model — URL-embedded token, no header management 30-minute end-to-end — HTTP Source URL is the only credential needed Verified on Sumo Logic JP region. Logs searchable via _sourceCategory=aws/fsxn/audit . This is Part 12 of the Serverless Observability for FSx for ONTAP series. Why Sumo Logic for Japanese Enterprises? For organizations operating under Japanese data protection regulations, the choice of observability platform often comes down to one question: where does the data physically reside? Requirement Sumo Logic JP Other Options Data residency in Japan ✅ Tokyo deployment Varies by vendor APPI compliance consideration ✅ Data stays in JP May require cross-border assessment Free tier for validation ✅ 500 MB/day Most offer 14-day trials only No agent installation ✅ HTTP Source (agentless) Some require collectors Sumo Logic's JP deployment ( service.jp.sumologic.com ) processes and stores all data within Japan, making it a straightforward choice for organizations that need to demonstrate data residency compliance. Compliance note : This integration provides a technical path for data residency. Evaluate your specific regulatory requirements with your compliance team — data residency alone does not constitute full regulatory compliance. Architecture ┌────

TL;DR We built a serverless Lambda pipeline that ships FSx for ONTAP audit logs to Dynatrace via the Log Ingest API v2. The real value: Dynatrace's Davis AI can automatically correlate file access anomalies with application performance degradation — answering "why is the app slow?" with "because 500 users hit the same NFS share simultaneously." FSx for ONTAP → S3 Access Point → EventBridge Scheduler → Lambda → Dynatrace Log Ingest API v2 │ ▼ Davis AI ┌───────────────────┐ │ Correlates: │ │ • File access │ │ anomalies │ │ • APM metrics │ │ • Infrastructure │ │ health │ │ │ │ → Root cause │ │ in seconds │ └───────────────────┘ Verified on Dynatrace SaaS Trial (Tokyo-equivalent region). Logs visible in Logs Viewer within 1-2 minutes. This is Part 11 of the Serverless Observability for FSx for ONTAP series. Why Dynatrace for FSx for ONTAP? Most observability tools treat storage logs as isolated data. Dynatrace is different — it builds a topology map of your entire stack and uses Davis AI to find causal relationships through time-window correlation and entity connectivity: Scenario Without Dynatrace With Dynatrace App latency spike "Check the logs" Davis AI detects temporal correlation: file access to /vol/data/ increased 10x within the same 5-minute window as app response time degradation, connected via topology (app → NFS mount → SVM) Storage I/O anomaly Manual investigation Automatic correlation via shared topology entities — Davis identifies which services are affected based on entity relationships User reports slow file access Grep through audit logs DQL query + topology view showing the full dependency path from user request to storage operation The key differentiator: Davis AI correlates events across entities that share topology connections within overlapping time windows — not just keyword matching or manual dashboard correlation. Architecture ┌─────────────────────────────────────────────────────────┐ │ Event Sources │ ├─────────────────────────────────────────

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