ClickHouse

Billions of rows.
One conversation.

Connect to your ClickHouse cluster and build real-time dashboards, log analysis, and scheduled reports — just by asking.

events_log — ClickHouse
2026-03-13 14:02:31.847INFOapi-gatewayGET /v1/events 200 12ms
2026-03-13 14:02:31.923INFOauth-servicetoken validated user_id=8847291
2026-03-13 14:02:32.104WARNpayment-svcretry attempt 2/3 for charge ch_3N8x...
2026-03-13 14:02:32.218INFOapi-gatewayPOST /v1/events/batch 200 45ms (2,847 rows)
2026-03-13 14:02:32.445ERRORsearch-svctimeout after 5000ms query_id=q-9f2e...
2026-03-13 14:02:32.671INFOapi-gatewayGET /v1/dashboards/prod-metrics 200 8ms
2026-03-13 14:02:32.889INFOuser-serviceprofile updated user_id=9912034
2026-03-13 14:02:33.012INFOingest-workerbatch committed 14,291 events in 0.3s

Speed you can feel.

2.3B
rows scanned
0.8s
query time
1
message to dashboard
no row limit

What analytics engineers build with camelAI.

Real-time dashboards

Monitor requests per second, error rates, and latency across services. camelAI builds live-refreshing dashboards you can share with your team — no Grafana configuration required.

Log analysis

Search and visualize billions of log entries. Find errors, trace request flows across services, and spot anomalies — all in one conversation. Like grep, but it understands your question.

Event analytics

Funnel analysis, user paths, conversion metrics — on your clickstream data. Get Sankey diagrams and cohort breakdowns without writing a single GROUP BY.

Scheduled reports

Set up cron jobs that query fresh ClickHouse data on a schedule, generate updated reports, and alert you in Slack when metrics cross a threshold.

From query to dashboard in one message.

Service Monitoring — ProductionLive
Requests / sec
12,847
▲ 12% vs yesterday
P99 Latency
142ms
▼ 8% vs yesterday
Error Rate
0.03%
── stable
Throughput
2.4 GB/s
▲ 5% vs yesterday
Requests per Second (last 24h)
15k10k5k0
00:0004:0008:0012:0016:0020:00

What will you build on your ClickHouse data?

Connect to our ClickHouse cluster and build a real-time dashboard showing requests per second, p99 latency, and error rates by service. Refresh every 5 minutes via cron.

Try this prompt

Query the events table — 2.3 billion rows — and show me a funnel analysis: page_view → signup → first_purchase → repeat_purchase, broken down by acquisition source.

Try this prompt

Analyze our clickstream data from the last 7 days. Find the top 10 user paths through the product and visualize them as a Sankey diagram.

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Pull hourly error counts from our ClickHouse logs for the past month. Highlight anomalies and set up a cron job that alerts me in Slack when error rates spike.

Try this prompt

Your data is already fast.

Now make it useful.