Apache Kafka®️ 비용 절감 방법 및 최적의 비용 설계 안내 웨비나 | 자세히 알아보려면 지금 등록하세요
Real-Time Streaming for Telemetry & Observability Pipelines
Control telemetry before it reaches your observability tools — reduce ingest costs, stay visible through spikes, and reuse data across tools and teams.
Observability Gets Harder as You Scale
As telemetry volumes grow, costs rise, flexibility decreases, and visibility can break down when teams need it most.
Runaway Ingest Costs
As observability data grows, rising ingest and storage costs can force teams to drop data, shorten retention, or limit what gets indexed.
Tool-Coupled Pipelines
Proprietary agents and platform-specific data flows make it difficult to add, replace, or run tools in parallel without re-instrumenting applications or rebuilding pipelines.
Visibility Gaps During Spikes
Failures can trigger sudden surges in observability data, overwhelming brittle pipelines and causing bottlenecks or data loss when teams need visibility most.
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Take Control of Your Observability Data
Confluent provides a vendor-neutral, real-time streaming foundation to help reduce costs, preserve visibility, and keep observability data flexible and reusable.
Control Observability Cost
Filter, transform, aggregate, and route observability data before it reaches downstream tools —reducing unnecessary ingest while preserving the signals you need.
Preserve Visibility Under Pressure
Buffer and route high-volume observability data during spikes, helping teams maintain access to critical signals when they need them most.
Keep Observability Data Flexible and Reusable
Decouple observability data from individual tools so you can route and reuse it across teams, systems, and use cases — without rebuilding pipelines as requirements change.
A Streaming Foundation for Observability
Confluent provides the real-time streaming layer between observability data sources and downstream tools, so you can move, process, govern, and route data at scale.
Stream
Handle high-volume observability data cost-efficiently with Freight clusters in Confluent Cloud or WarpStream in your cloud.
Connect
Ingest observability data from applications, infrastructure, collectors, and existing tools into a vendor-neutral streaming layer that works with your current stack.
Process
Use Apache Flink® to filter, enrich, aggregate, and route observability data in real time—reducing unnecessary downstream ingest.
Govern
Apply schemas, data quality rules, and access controls to keep observability data consistent, trusted, and governed across your stack.
Reference architecture
Step 1 - Ingest
Logs, metrics, traces, and events are ingested into Confluent Cloud via OpenTelemetry exporters or the Kafka API.
Step 2 - Stream
Telemetry is durably buffered and distributed through Kafka topics, decoupling producers from downstream processing and consumers.
Step 3 - Process
Logs and metrics are filtered, enriched, windowed, and aggregated in real time with Apache Flink on Confluent Cloud.
Step 4 - Deliver
High-throughput telemetry is delivered to downstream observability and monitoring platforms, or offloaded to object storage for long-term retention and analytics.
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