I recently came across a Reddit post from a data engineer working on a real-time pipeline for a logistics client. The goal is to ingest millions of IoT events per hour from their vehicle fleet, including GPS, engine status, temperature, and more. They’re currently using Kafka, Debezium, and Snowflake to process this data, but they’re running into some major issues as the data scales.
The first challenge they’re facing is late-arriving events from edge devices in poorer connectivity zones. Even with event timestamps and buffer logic in Spark, they’re ending up with duplicated records or gaps in aggregation windows.
The second issue is schema drift. Whenever the hardware team updates firmware or adds new sensor types, the structure of the incoming data changes, breaking things downstream. They’ve tried enforcing Avro schemas via Schema Registry, but it’s not working well when things evolve quickly.
To make matters worse, their Snowflake MERGE operations are starting to fail under load. Clustered tables are helping, but not enough.
The team is debating whether to continue building around this setup with more Spark jobs and glue code or switch to a more managed solution that can handle real-time ingestion and late arrival tolerance. They’re looking for insights on how to get out of this mess.
Have you ever faced similar challenges in your data pipeline? How did you overcome them? Share your thoughts and experiences in the comments below.