AIOps Hub - LumiOps

4,287 events an hour.
12 incidents.

Deduplication, rules and cross-source correlation collapse the alert storm into a handful
of real incidents — each mapped to the service it affects, with the events that formed it
still attached.

EVENT PIPELINE & DEDUPLICATION

Four stages between noise and
signal.

Nothing is thrown away — every raw event is kept and attached to the incident it
belongs to. What changes is how many things a human has to look at.

01 · RAW EVENTS

4,287

Everything arriving from every source in the last hour — metrics, logs, pages, cert checks.

02 · AFTER DEDUP

856

80.03% removed. The same failure reported ten times by four tools becomes one
alert.

03 · AFTER RULES

Four rules firing suppression windows, maintenance, severity thresholds and known-noise filters.

04 · INCIDENTS

Correlated across sources and mapped to a service. 99.72% reduction, ~71 events each.

CROSS-SOURCE SIGNAL CORRELATION

One failure, four tools shouting.

A DB pool exhaustion shows up as a Datadog error, a PagerDuty page, a Kubernetes restart and a latency warning. Those aren’t four problems — and correlation is what proves it.

1

Fingerprint matching

Identical events from the same source collapse instantly — that’s the 80% dedup.

2

Temporal & topological links

Events close in time on related entities group even when the wording differs.

3

Cross-source joins

A Datadog metric and a PagerDuty page about the same service become one incident.

4

Evidence preserved

Every contributing event stays attached, so the RCA writes itself.

inc_01XY9M2X · checkout-api14 events · 4 sources

datadog · DB connection pool exhausted (max=10)×6

pagerduty · Page on-call: web-prod-3×2

k8s · checkout-api pod restart ×3×3

datadog · p95 latency on db-main-1 > 800ms×3

Root cause · connection pool exhaustion

A long-running report query held 8 of 10 pool slots from 14:00. Pod restarts and the on-call page are downstream effects, not separate faults. Mapped to service checkout-api.

INCIDENT → SERVICE MAP12 open

checkout-api14 ev · OPEN

payments7 ev · OPEN

web-prod-34 ev · OPEN

cert-renewer3 ev · ACK

Why service mapping matters

An incident attached to checkout-api routes to the team that owns it, inherits its SLA, and shows business impact immediately. An incident attached to a hostname does none of that.

SMART TRIAGE & TIME WINDOWS

Slice it however you’re working.

Filter by status, severity, host or source, and move the window from 15 minutes to 24 hours — the pipeline recalculates against exactly what you’re looking at.

1

Status & severity filters

Open, acknowledged, resolved; critical, warning, info — with live counts on every chip.

2

Time-windowed views

15m for an unfolding incident, 24h for the pattern behind it.

3

Host and source scoping

Narrow to one noisy source to see whether it’s the tool or the system.

4

Incident-to-service mapping

Every incident carries the service it affects, not just the box it happened on.

CAPABILITIES

What the AIOps engine does.

⇥

Event pipeline & dedup

Four stages from raw event to incident, with 80%+ removed at the dedup stage alone.

⚙

Rules-based processing

Suppression windows, maintenance calendars, thresholds and known-noise filters.

◉

Live activity stream

Every event as it lands, tagged deduped, grouped or new in real
time.

⏱

Time-windowed views

15m, 1h, 6h and 24h — the whole pipeline recalculates per window.

⇄

Cross-source correlation

Datadog, PagerDuty, Kubernetes and cert checks joined into single incidents.

◈

Incident grouping & RCA

Contributing events stay attached, so root cause is evidenced rather than asserted.

▲

Smart triage & filtering

Status, severity, host and source filters with live counts.

🔗

Incident-to-service mapping

Each incident tied to the service it affects, for routing, SLA and impact.

▤

Nothing discarded

Raw events are retained and queryable, even after they’re collapsed.

WHY IT MATTERS

Alert fatigue is a reliability risk.

When on-call sees 4,287 events an hour, the real one gets missed — not through
carelessness, but because no human triages at that rate.

WHAT ON-CALL SEES

4,287 events

12 incidents

99.72% reduction, with every raw event still attached.

DUPLICATE NOISE

80% of the stream

collapsed

The same failure reported by four tools becomes one.

TIME TO ROOT CAUSE

correlate by hand

already grouped

Contributing events arrive pre-assembled as evidence.

ROUTING

by hostname

by service

Straight to the owning team with the right SLA.

👤

For the on-call engineer

DAY TO DAY

✓

A page you can trust

Twelve incidents an hour is a workload. Four thousand events is background radiation you learn to ignore.

✓

Evidence arrives with the incident

The fourteen events that formed it are attached — no hunting across four tools.

✓

Rewind the window

Flip to 24h to see whether tonight’s alert has been building
all week.

✓

Nothing is lost

Deduped doesn’t mean deleted — the raw stream is still there when you need it.

◈

For the organization

ON THE BALANCE SHEET

✓

Missed-signal risk falls

The most expensive outages are the ones where the alert fired and nobody could see it.

✓

Smaller on-call rotations

Twelve real incidents an hour is coverable by a team you can actually staff.

✓

Service-level accountability

Incidents mapped to services make ownership and SLA reporting real.

✓

Keep your existing tools

Datadog, PagerDuty and the rest keep running — this correlates across them.

Console figures are illustrative and stream as a demo. Reduction ratios depend on your sources and rule set.

Get started

Run last night’s alert
storm through it

Point Lumi at a real hour of your event stream and see how many
incidents were actually in there.

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