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Real-Time Observability Pipelines for the ClickHouse Era

Transform, deduplicate, and enrich your logs, metrics, and traces in flight. Build a high-performance ClickStack with GlassFlow to eliminate data silos and spiraling costs.

Try for free

Book a demo

Real-Time Observability Pipelines for the ClickHouse Era

Transform, deduplicate, and enrich your logs, metrics, and traces in flight. Build a high-performance ClickStack with GlassFlow to eliminate data silos and spiraling costs.

Try for free

Book a demo

Real-Time Observability Pipelines for the ClickHouse Era

Transform, deduplicate, and enrich your logs, metrics, and traces in flight. Build a high-performance ClickStack with GlassFlow to eliminate data silos and spiraling costs.

Try for free

Book a demo

Why Traditional Observability Fails

Exploding observability data volumes are draining budgets and bloating databases. Transform it in the pipeline — before it costs you.

Silent Failures

Without a native Dead Letter Queue, your pipeline can silently pollutes ClickHouse with corrupt data.

Duplicate Data

Bloating storage and skewing analytics.

Schema Evolution

Struggling with nested JSON on ClickHouse and evolving OTel schemas.

Your Intelligent Observability Pipeline with GlassFlow

Real-Time Deduplication

Remove 15–30% of redundant log data with GlassFlow before it hits ClickHouse.

Real-Time Deduplication

Remove 15–30% of redundant log data with GlassFlow before it hits ClickHouse.

Streaming Contextual Joins

Enrich logs with metadata (e.g., user IDs from a separate stream) in milliseconds.

Streaming Contextual Joins

Enrich logs with metadata (e.g., user IDs from a separate stream) in milliseconds.

Automatic Schema Mapping

Flatten nested JSON from OpenTelemetry automatically for ClickHouse native performance.

Automatic Schema Mapping

Flatten nested JSON from OpenTelemetry automatically for ClickHouse native performance.

Automatic Schema Mapping

Flatten nested JSON from OpenTelemetry automatically for ClickHouse native performance.

Cost-Efficient Scaling

Replace heavyweight Java/JVM streaming tools (Apache Flink, Kafka Streams,Kafka Connect)with a lightweight, streaming engine like GlassFlow.

Cost-Efficient Scaling

Replace heavyweight Java/JVM streaming tools (Apache Flink, Kafka Streams,Kafka Connect)with a lightweight, streaming engine like GlassFlow.

OpenTelemetry Pipelines with GlassFlow & ClickHouse

Build a ClickHouse Telemetry Pipeline with OpenTelemetry and GlassFlow

GlassFlow provides the missing 'Processing' layer between your OTel Collector and ClickHouse, ensuring your observability pipelines remain fast, clean, and cost-effective.

Read the Guide

Observability Tools Comparison

Stateful Processing

Complex Joins

ClickHouse Native

Developer Experience

Native (7-day window)

Python SDK

Native Protocol Optimized

Code-native (Python SDK/UI)

Traditional Pipelines

(vector.dev/Fluentbit)

Limited

Basic

Config-heavy

Learn More About ClickHouse Optimization

Telemetry pipeline, OpenTelemetry, observability pipeline, GlassFlow, ClickHouse pipeline, ClickHouse observability, OTel, OTel Collector, data masking

Tutorials

Build a ClickHouse Telemetry Pipeline with OpenTelemetry and GlassFlow

This guide shows you how to stream OTLP data, perform deduplication and PII masking with GlassFlow and send data directly into ClickHouse for high-performance, low-cost observability.

Written by

Armend Avdijaj

Product Updates

Clean, enriched OTel data in ClickHouse — without the glue code

GlassFlow now supports native OpenTelemetry ingestion — so your traces, logs, and metrics arrive in ClickHouse deduplicated, enriched, and query-ready

Written by

Armend Avdijaj

Fraud detection pipeline, Fraud detection use case, fraud detection, Kafka, GlassFlow, ClickHouse, stream processing, data pipelines, anomaly detection

Tutorials

Fraud Detection Pipelines with Kafka, Glassflow, and ClickHouse

A demo using GlassFlow to deduplicate and filter Kafka events before they reach ClickHouse for real-time fraud detection.

Written by

Armend Avdijaj

I was fed up seeing my engineers debugging broken observability pipelines and spending their time creating custom solutions, while our focus should have been on improving our own product.”