Building an IoT Sensor Data Lake for Hardware Telemetry
To support custom manufacturing endpoints and monitor the health of 3D printing farms, I needed a pipeline for collecting telemetry data from hardware sensors (e.g., pH monitors, Klipper temperatures) for long-term analytics and dashboarding[cite: 13].
The Architecture Flow
[ Hardware Sensors ] → [ IoT Core (MQTT) ] → [ Kinesis Firehose ]
↓
[ QuickSight Dashboards ] ← [ Amazon Athena ] ← [ Amazon S3 Data Lake ]
1. Ingestion & Routing
Microcontrollers send JSON payloads via MQTT directly to AWS IoT Core[cite: 21]. I configured IoT Core rules to route these messages seamlessly to an Amazon Kinesis Data Firehose delivery stream[cite: 21].
2. Transformation & Storage
Before dropping the data into the Amazon S3 Data Lake, Kinesis Firehose batches the incoming JSON and converts it into the Apache Parquet format[cite: 21]. Converting to Parquet reduces S3 storage costs and Athena query times.
3. Serverless Querying
Instead of running an expensive relational database, I query the S3 bucket directly using Amazon Athena[cite: 22]. Athena uses standard SQL, meaning I only pay for the queries I run, scaling down to zero cost when not in use.
Firehose introduces a buffering delay (minimum 60 seconds), so this pipeline is not suitable for real-time alerting[cite: 24]. For instantaneous alerting, Kinesis Data Streams would be required, but Firehose is the superior choice for cost-effective, long-term analytical storage.