dltHub + Edge Analytics + AI Agents = Factory of the Future!

I recently explored how dltHub can revolutionize IIoT edge analytics — and the immense possibilities! Being a huge fan of DuckDB , I decided to build a solution that processes MQTT messages from an EMQ Technologies broker and transforms them into actionable insights.

Here’s what I built:
🔗 A dlt pipeline that:

1️⃣ Monitors MQTT messages published to topics in an EMQX broker.
2️⃣ Saves these messages into CSV files organized by date.
3️⃣ Uses DuckDB to run analytics at the end of the day, transforming data for edge insights and uploading results to an AWS S3 bucket.

💡 What’s exciting?
This simple workflow opens up powerful use cases:

📊 Knowledgebases: With tools like Amazon Web Services (AWS) Bedrock and Streamlit , teams can query the transformed data to get instant answers:

“What were the readings of Machine A yesterday?”
“Could you provide me anomalies”
“What was the minimum pressure recorded?”

🤖 Agentic Apps: Imagine uploading machine manuals and having an app suggest actions when specific conditions arise, like:

“If pressure drops below X, alert the team and recommend troubleshooting steps.”

🌟 This setup isn’t just efficient — it’s a step toward the factory of the future, where teams get insights instantly without waiting for external support.

I’m also planning to contribute to dltHub by adding MQTT as a native data source, making this integration even smoother.

This is particularly exciting for Data Engineers, offering vast opportunities to create impactful and meaningful data products.