More data requires greater observability

As the complexity of applications and infrastructure grows, monitoring complex systems is driving the need for observability platforms to provide contextual insights from large amounts of complex data.

It is increasingly becoming a critical requirement to have an observability platform that can ingest and retain logs, metrics, traces, profiling, and business data at high cardinality and dimensionality for a longer period of time. Retaining your data in full fidelity allows SRE teams to proactively analyse trends and patterns via analytics or machine learning (ML) capabilities to avoid service disruption and downtime.

As an organisation's need for increased visibility grows, it is important to consider the broadest range of telemetry types to ensure full coverage. Given the exponential increase in the volume of data generated, solutions must be architected to surface relevant insights at scale.

AI is changing how observability is done

AI encompasses ML and the evolving generative AI. ML capabilities within observability drive anomaly detection for faster root cause analysis. However, ML-driven anomaly detection has limitations and often results in false alerts and manual troubleshooting due to the complexity and variety of data collected. The limitations include: data retention, uniqueness of telemetry data types, different data formats, and limitations of built-in ML models.

Generative AI is transforming observability from a manual, tedious process of data exploration and correlation for root cause identification — often requiring intervention from multiple experts — into a more intuitive and intelligent workflow-driven process for problem resolution. This advancement allows operations teams to understand system behavior and impact without needing in-depth knowledge, surfacing relevant insights that enable auto-detection, diagnosis, and remediation of issues.

As Rory Stewart discussed on The Rest is Politics podcast on 24 October 2024, the NHS can continue to mobilise data to create better patient outcomes. But while generative AI offers promise, it is also susceptible to hallucinations, and large language models (LLMs) without the context of private data can often be less meaningful or just plain misleading.

Full-stack observability shouldn’t be cost-prohibitive

An effective observability platform is dependent on its ability to ingest and retain all observability data for a longer duration without being cost-prohibitive. Often, operations and development teams have to compromise on multiple dimensions due to increasing costs from:

• Complicated pricing schemes and SKUs where observability capabilities are priced separately, resulting in unforeseen costs
• The need to increase data retention periods for each telemetry type to meet business needs, often incurring additional costs
• An exponential increase in storage costs when adding and using necessary, custom metadata

One common solution is to only monitor Tier 1 applications in your environment, but these compromises can often lead to operational blind spots. The potential impact? Customers identifying performance issues before operations teams do, finger-pointing, along with slower problem identification and resolution.

With Elastic Observability, your organisation can attain unmatched scale and analytics at reduced costs with a unified data store, high-performance data tiers, and distributed search delivering a single pane of glass experience. Eliminate monitoring blind spots cost-effectively by ingesting and storing high cardinality data with limitless retention.