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.
Related articles
More from Imply on the same topic.
Search your Splunk DDSS archive without restoring it
Security teams often keep logs long after they have aged out of their SIEM platform’s searchable window. The data is there for investigations or audits, but accessing the data for insights can be complicated.
Claude makes dashboards now, Lumi is ready for them
This week Anthropic launched Claude Dashboards in beta. You connect a data source, ask a question in plain language, and Claude writes the queries and builds the dashboard.
Lumi Lounge Recap: 3 Takeaways from Splunk .conf26
The Imply Lumi Lounge sat directly across the street from Splunk .conf26 and reached venue capacity on both days. Digital signs around the Colorado Convention Center pointed attendees our way with a simple message: “Federated search, done right. Full SPL. Less spend.”
Agents don’t query like your detections do
A security team of four has run a major SIEM since February. Nobody on the team writes SPL. Nobody opens the console or looks at the dashboards. Every question goes through an AI agent, which writes the query, runs it, reads the result, and answers in plain language.
Why You Shouldn’t Have to Delete Your VPC Flow Logs
When a security incident happens, investigators almost always start with the same questions: Which systems communicated? Where did the traffic originate? What changed before the incident? Was data exfiltrated? AWS VPC Flow Logs are often the fastest way to answer those questions.
Splunk Smartstore vs Lumi Loglake: Two Very Different Ways to Search Logs in Object Storage
One copies data back before it can be searched. The other queries it where it lives. Lumi Loglake lets Splunk teams query logs directly in object storage, including AWS S3, Delta Lake, Apache Iceberg, using standard SPL.
Supercharging Schema-On-Read: Logs in Object Storage Don’t Need a Data Catalog
Machine data architectures are rapidly changing. As telemetry volumes continue to grow and as costs rise, organizations are increasingly moving logs and other machine data into object stores such as AWS S3.
Imply Lumi Loglake vs Splunk Federated Search for S3
Teams are increasingly moving log data into AWS S3 to reduce costs and extend retention. Both Lumi Loglake and Splunk Federated Search to S3 help you query data in AWS S3 to lower costs, however the two technologies have several notable differences.
A First Look at Lumi Loglake: Query Logs Where They Live
TL;DR: Imply Lumi Loglake is a lakehouse (separated compute/storage) architecture for unstructured logs that reduces costs from 40% up to orders of magnitude on your hardware/AWS/Azure bill used to run your SIEM/observability products.
Imply Lumi Major Release Preview: Continuing the Journey Towards Decoupled Observability/SIEM
We are getting ready to introduce the next major expansion of Imply Lumi and the observability warehouse. When we introduced the industry’s first observability warehouse, the goal was clear: decouple the observability stack by separating storage, compute, and access.
Query Lumi from Grafana: Now in Private Preview
Observability has a silo problem. Today, we’re removing another one. Imply Lumi’s Grafana Loki integration is now in Private Preview.
