It’s Time to Rethink Observability: The Event-Driven Future

Observability has evolved.

Forward-looking teams are already moving beyond static dashboards and fragmented telemetry—treating all observability data as events and unlocking real-time insights across their stacks. As architectures grow more complex, observability must operate as a real-time intelligence layer: one that enables teams to detect, understand, and act as issues arise—not after the fact.

We’re past the era of just collecting more data.

The frontier now is understanding and acting on events in real time—at scale, with speed, and with precision.

Why Traditional Observability Falls Short

Today’s organizations don’t just collect observability data—they drown in it. Yet, despite an explosion in data volume, many teams still struggle to extract real-time, actionable insights. 

Soaring Costs 

• High storage and indexing costs force teams to sample or discard valuable data.
• Long-term retention is technically possible—but often cost-prohibitive, leading to limited visibility over time.
• Confusing licensing makes costs unpredictable—and scale unaffordable.

Slow and Unpredictable Queries 

• High-cardinality data—like user sessions, IPs, and trace IDs—slows queries to a crawl.
• Query performance degrades as data scales—making it hard to predict when or where delays will occur.

Siloed Data = Missed Signals

• Logs, metrics, and traces live in separate tools and databases.
• Teams are left stitching together root causes manually, often too late.

Reactive, Not Proactive

• Static dashboards and rule-based alerts miss issues in dynamic systems.
• Teams operate in post-mortem mode instead of preventing incidents in real time.

The Shift to Event-Driven Observability

The leading edge of observability doesn’t treat logs, metrics, and traces as separate datasets—it unifies them into a single stream of events for real-time analysis and insight.

Streaming Analytics: Ingest and analyze logs, metrics, and traces as they happen—without batch delays.

Event-Centric Correlation: Automatically link related events across services to uncover root causes faster.

AI-Driven Anomaly Detection: Identify performance degradation and security threats in milliseconds.

Cost-Efficient, Full-Fidelity Storage: Capture and retain complete observability data at scale—without breaking the budget or compromising on detail.

What’s Different?

Traditional Observability	Event-Driven Observability
Logs, metrics, and traces stored in separate systems	All observability data ingested into a unified real-time event stream
Manual correlation or brittle custom pipelines	Automatic, AI-powered correlation surfaces insights instantly
Queries are slow and costly, especially for high-cardinality data.	Queries are fast and efficient—optimized for real-time and historical analysis
Aggregated metrics reduce fidelity and context	Raw events retain full detail—no loss from pre-aggregation
Short retention windows due to cost	Full-fidelity storage without unpredictable expenses
Static dashboards and rule-based alerts	Real-time anomaly detection powered by AI

Real-World Adoption: How Leading Companies Are Evolving Observability

Netflix: Scaling Observability with Event-Driven Architectures

Netflix operates one of the largest microservices infrastructures globally. By embracing real-time event analysis with Apache Kafka for ingestion, object storage for retention, and Apache Druid for high-speed querying, Netflix detects anomalies instantly and ensures seamless user experiences.

Ibotta: Cutting Costs While Improving Query Performance

To fight real-time fraud across its 120 million users, Ibotta replaced slow, siloed analytics with a new event-driven architecture built on Apache Druid and Imply. They now stream flattened event data from Kinesis into Druid, giving ops teams self-serve access to real-time insights.

The result? Faster fraud detection, fewer incidents, and a platform that’s now expanding into ad ops, clickstream analysis, and more.

Reddit: Detecting Fraud in Real Time

With millions of interactions per second, Reddit must detect fraudulent activity instantly. Their event-driven observability system, built on Druid, enables real-time anomaly detection across high-cardinality data, reducing fraud response time and improving platform security.

Powering the Future of Observability with Imply 

Traditional observability tools are fragmented, reactive, and expensive. The future is event-driven: a modern observability stack that treats all telemetry as real-time events, applies AI to surface anomalies instantly, and scales without runaway costs.

Imply is purpose-built to power this future.
Built on Apache Druid, Imply adds the cloud-native capabilities, operational simplicity, and cost-efficiency that modern observability demands. With Imply, teams can:

• Run sub-second queries on high-cardinality, real-time event data—at any scale
• Stream and analyze data in real time while retaining full-fidelity historical context
• Analyze both streaming and batch data through a single, unified interface
• Lower costs dramatically by offloading workloads from expensive SIEM and log tools
• Rely on always-on availability with fault tolerance, auto-scaling, and no-ops infrastructure
• Get started fast with Polaris—our fully managed Druid-as-a-Service, available across multiple clouds

Stop Collecting More.  Start Understanding More.

Imply helps you detect, respond, and act—in real time.
No infrastructure to manage. No compromises on performance. No surprise bills. Start your free trial of Polaris—with $500 in free credits to get going fast.