How to Build a Data Quality Framework for IoT Telemetry

Handling IoT device data can get messy fast. With over 2,500 live devices under my belt, building a data quality validation framework became essential. This framework ensures your data is accurate and reliable before you move further. I'll walk you through an 11-step pipeline I built. Prerequisites You'll need Python 3.10+ installed and some API keys, depending on your data source (e.g., AWS or Azure). Also, familiarity with pandas and n8n will help. Installation/Setup Begin by installing the necessary Python packages. Run: pip install pandas == 1.3.5 numpy == 1.21.4 n8n == 0.147.0 Common error? If you hit a "module not found" issue, ensure virtual environments aren't messing things up. A simple pip list can help you spot missing packages. Building the Framework This 11-element pipeline starts with fetching raw data and ends with storing results. I'll highlight the critical parts. Step 1: Fetching the Data Set up a node in n8n to grab data from your IoT devices. I use HTTP nodes, but M