Healthcare observability is under pressure. Data volumes are exploding, uptime requirements are non-negotiable, and infrastructure costs continue to rise—yet most environments are still built on inefficient data retention and static architectures.
In this session, we take a strategic look at how modern observability approaches—powered by optimized Index Lifecycle Management (ILM) and automation—enable healthcare organizations to achieve both high availability and cost control.
We’ll show how to design observability systems that scale with patient-critical workloads, reduce wasted infrastructure spend, and ensure consistent performance across complex environments.
- Why uptime is a clinical, financial, and operational necessity in healthcare
- How idle data and over-retention silently drive infrastructure costs
- How optimized ILM reduces storage costs without compromising availability
- How Observability as Code ensures consistency, compliance, and scalability
- What high-performing healthcare observability architectures do differently
Optimizing data retention across high-volume healthcare systems; reducing storage costs without sacrificing access to critical patient data; improving uptime for life-critical platforms; accelerating issue detection and response; and scaling observability securely across distributed, compliance-driven environments.
- Designing ILM strategies for high-ingestion healthcare environments (logs, metrics, traces)
- Balancing uptime, cost, and compliance across observability architectures
- Automating observability workflows using version-controlled configurations
- Reducing operational overhead for platform and infrastructure teams
- Improving system reliability and faster issue resolution in mission-critical systems
Speakers
Sarvagya is an Elastic Engineer at Hyperflex, and an Elastic Certified Observability Engineer with a strong track record in designing scalable data pipelines, building impactful Kibana dashboards, and improving system observability for enterprises. He brings deep expertise in Elasticsearch, Logstash, and Kibana, along with hands-on experience leading anomaly detection, Elastic ML jobs, optimized clusters for performance and cost efficiency, and enabled teams to unlock the full potential of Elastic for monitoring and alerting.
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Ploy Wongtaladkwon is a Marketing Manager at Hyperflex with a background in social media marketing, content strategy and B2B marketing. Ploy’s career spans marketing strategy, content creation, and event coordination. She holds a MS in Marketing Analysis from DePaul University, Chicago, IL, United States.