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Logfile Roundup
Logfile Roundup
@LogfileRoundup

BigQuery vs ELK for a log pipeline

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BigQuery vs ELK for a log pipeline

Where to park months of access logs? The two camps, fairly summarized.

🔗 BigQuery — JR Oakes and others have shown the serverless angle: dump logs to a table, pay per query scanned, never run a server. Wins for SEOs who query weekly, not hourly.
🔗 ELK (Elastic/Logstash/Kibana) — the Elastic docs make the real-time case: live tailing, Kibana dashboards, alerting on 5xx spikes. Wins when you're watching logs as they land.

⭐ Pick of the week: the cost-control tip from BigQuery's own guide — partition by date and cluster by URL, or a single broad query scans the whole table and bills you for it.

Takeaway: BigQuery for cheap historical analysis at SEO cadence; ELK when you need eyes on logs in real time.
Этот пост опубликован в Telegram-канале Logfile Roundup. Подписаться можно по ссылке: @LogfileRoundup.

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