Cross-team — Cross-team Platform Review

SME Track: All Teams

Presenter: Roberto Pantoja

Date: 2026-11-04

SLB × Elastic Workshop Program

Overview

Cross-team session reviewing adoption progress and next steps.

Where this applies

These labs run on Observability Serverless — a fully managed project so you can practice without cluster operations.

The same capabilities you explore here — ES|QL, Streams, AI Assistant, Agent Builder, Workflows, and SLOs — are available on Elastic Cloud Hosted (ECH) and self-managed deployments.

Serverless mainly saves operational toil (sizing, ILM, Fleet, upgrades). Your observability skills transfer directly.

Session topics

  • Elastic adoption progress across all teams
  • Open Q&A and live troubleshooting
  • Next steps and program evolution

Why these features?

Observability everywhereSame value on Serverless, ECH, and self-managed
Elastic StreamsManaged routing and processing for telemetry
ES|QLOne query language for logs, metrics, and traces
SLOsUser-facing reliability, not just green dashboards
AI AssistantNatural language over your live telemetry
WorkflowsAutomate alert response safely

Use → to see why each feature matters for SLB.

Why Observability everywhere?

Same value on Serverless, ECH, and self-managed

Without it

Teams treat deployment choice as a feature fork — assuming Serverless skills won't transfer to ECH or on-prem, or that only one model fits SLB.

With Observability everywhere

Serverless, Elastic Cloud Hosted, and self-managed share the same Observability UX. Labs use Serverless to skip cluster toil; you apply the same ES|QL, Streams, and AI workflows wherever Elastic runs.

  • Serverless: Elastic manages scaling, upgrades, ILM, and Fleet overhead
  • ECH / on-prem: same capabilities when you need full infrastructure control
  • Choose deployment for ops burden — not for observability feature access
Your deployment
Same Kibana
Same skills

Why Elastic Streams?

Managed routing and processing for telemetry

Without it

Custom ingest pipelines, index templates, and rollover policies per team — fragile, hard to govern, and different on every cluster.

With Elastic Streams

Streams define how logs, metrics, and traces are routed, processed, and retained — on Serverless, ECH, and self-managed, with a unified UI.

  • Reduce DIY pipeline + ILM work with declarative stream rules
  • Consistent ownership and naming across SLB domains
  • On self-managed/ECH you may still use ILM — Streams simplify routing either way
Ingest
Streams
Search & alerts

Why ES|QL?

One query language for logs, metrics, and traces

Without it

Different syntax per signal — PromQL for metrics, LogQL for logs, trace UI only — context switching slows incidents.

With ES|QL

ES|QL pipes data through filters, stats, and joins across observability datasets in Logs Explorer and Dev Tools.

  • Faster investigations with reusable query patterns
  • Same syntax in Logs Explorer on Serverless, ECH, and self-managed
  • AI Assistant can draft and explain ES|QL for your team
FROM logs-*
STATS / WHERE
Answer

Why SLOs?

User-facing reliability, not just green dashboards

Without it

CPU graphs look fine while customers see errors — no shared error budget or burn-rate language with product teams.

With SLOs

SLOs define availability/latency targets from real traces and metrics, with burn alerts before users flood support.

  • Align SRE and product on measurable reliability
  • Prioritize fixes when error budget is draining
  • Native in Observability on every deployment — no custom PromQL recording rules required
SLI signal
SLO target
Burn alert

Why AI Assistant?

Natural language over your live telemetry

Without it

Every investigator rebuilds the same ES|QL, scrolls dashboards, and writes runbook prose from scratch.

With AI Assistant

Ask questions in plain language — get ES|QL, summaries, and correlated logs/traces grounded in your project data.

  • Onboard new engineers without memorizing query syntax
  • Explain spikes and error patterns during live incidents
  • Draft queries you can save, share, and reuse
Question
AI Assistant
Evidence

Why Workflows?

Automate alert response safely

Without it

Manual Slack pings, ticket copy-paste, and runbook hunts — alerts fire but nothing moves until a human acts.

With Workflows

Workflows chain connectors (Slack, PagerDuty, webhooks) with approval steps when alerts or SLOs breach.

  • Notify the right channel with context automatically
  • Add human-in-the-loop before remediation scripts run
  • Reduce toil without bypassing change control
Alert
Workflow
Notify / act

Hands-on lab

Your lab uses Elastic Observability Serverless for a zero-ops learning environment.

The steps and features are the same on ECH and on-prem — follow the assignment panel when Kibana opens.

Instruqt track: slb-sme-all-teams

Resources

  • Registration: events.elastic.co/slbworkshops
  • Repo: github.com/poulsbopete/slb-workshops
  • Use ← → arrow keys to navigate slides