SensorFlow

Self-hosted event analytics with ClickHouse and SQL

2 followers

Self-hosted event analytics with ClickHouse and SQL

2 followers

SensorFlow connects event ingestion, ClickHouse storage, SQL modeling, and Apache Superset dashboards in a self-hosted stack. Start with a Docker demo, inspect raw events, and define metrics in SQL. Activate licensed ingestion to connect compatible Sensors Data SDKs. Keep your event data on infrastructure you control. SensorFlow is independent of Sensors Data.

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Launch tags:Analytics

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Hi Product Hunt! SensorFlow is for engineering teams that want control over their event data and a transparent path from ingestion to dashboards. The stack combines Go, ClickHouse, Redis, and Apache Superset. You can start with a Docker demo, explore clearly labeled sample events, and build SQL-based metrics before connecting real traffic. Real SDK ingestion requires license activation. We would love feedback on deployment, SDK integration, and the dashboards you need most. What is the hardest part of maintaining your event analytics pipeline?

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2mo ago

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28d ago

SensorFlow reading list: event counts, identity, and feature adoption

I work on SensorFlow. This is an AI-assisted summary of three tutorials we have already published, collected here for people checking their event-tracking definitions. It is a reading path, not a new product release.

Before trusting an analytics dashboard, I would separate three questions: which records qualify, what the identifiers represent, and which population had an opportunity to act. Fixing one of those does not automatically fix the other two.

1. Event count is not user count

A repeated delivery, a repeated action, and a second identifier are different things. Start by specifying the event filter, duplicate-delivery key, time window, and identity key. A distinct identifier count should not quietly become a claim about distinct people.

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