The document discusses Elastic Observability's solutions aimed at reducing mean time to recovery (MTTR) and improving data analysis with a unified approach. It highlights innovations such as integrated alerting, machine learning, and a centralized ingest management system that streamlines onboarding and empowers analysts. Elastic Observability aims to provide users with quick access to insights through hundreds of integrations and a common data schema.

1. 1
Elastic Observability
Drive MTTR towards Zero
Álvaro Lobato
VP Engineering | Elastic Observability

2. SaaS Orchestration
Elastic Cloud
on Kubernetes
Elastic Cloud Elastic Cloud
Enterprise
Elastic Enterprise Search Elastic SecurityElastic Observability
Kibana
Elasticsearch
Beats Logstash
Elastic Technology
Powered by the
Elastic Stack
3 solutions
Deployed
anywhere

3. 3

9. Our pricing is based only on the
underlying infrastructure
resources used by any given
deployment — across all data
types and use cases.
hosts
agents
functions
containers
metrics
logs ingested
Pricing per
Others
Entity-based pricing
Elastic
Resource-based pricing

10. Helping users embrace
observability since 2012

11. Helping users embrace
observability since 2012

12. Helping users embrace
observability since 2012

13. Helping users embrace
observability since 2012

14. Helping users embrace
observability since 2012

15. Helping users embrace
observability since 2012

16. Helping users embrace
observability since 2012

17. 17
Looking back

18. Reduce time to value and insights
100s of integrations
with data sources
Common data schema
and UI workflows
Through better connections across the board
Embedded alerting &
machine learning flows
Streamline
Onboarding
Enable
unified analysis
Empower
analysts

19. Simplify Onboarding
100s of integrations (and growing)
• Data to value in 5 minutes

20. Simplify Onboarding
100s of integrations (and growing)
• Data to value in 5 minutes
• Monitor public cloud and
cloud-native arch with ease

21. Enable Unified Analysis
Navigation between datasets
• Speed up troubleshooting and
root cause analysis
• Correlate across sources
through common schema
• Streamline investigation
through unified workflow:
‣ from metrics KPIs)
‣ to traces (dependencies)
‣ to logs (events)

22. Empower Analysts
Native alerting & anomaly detection
• Embedding alerting in context
of the use case
• Using machine learning to
streamline and automate tasks
in turnkey ways
– Anomaly detection
– Log categorization
– Schema inference

23. 23
Demo

24. 24
Looking ahead

25. Reduce time to value and insights
Unified ingest
management
Curated
observability reporting
Through better connections across the board
Reactive & proactive
investigative workflows
Streamline
Onboarding
Enable
unified analysis
Empower
analysts

26. Simplify Onboarding
Unified Ingest Management
• Single agent
• Integrations registry
• Centralized ingest management
• Streamline onboarding workflow

27. Enable Unified Analysis
Curated Observability Reporting
• Observability overview
• Service, infrastructure, and
end user health views
• Curated SLI/SLO reporting

28. Enable Unified Analysis
Curated Observability Reporting
• Observability overview
• Service, infrastructure, and
end user health views
• Curated SLI/SLO reporting
• Security analytics on top of
observability data

29. Empower Analysts
Investigative Workflows
• Alert libraries
• Advanced anomaly detection
• Root cause analysis
• Expanded set of actions

30. Thank you!