AUTOMATED DETECTION AND INTEGRATION OF DEEPLY NESTED DYNAMIC JSON FIELDS IN SNOWPARK FOR PROCESSING REAL-TIME TELEMETRY DATA

Abstract

This paper explains how to leverage the Snowpark Python API to set up a serverless, in-database pipeline in Snowflake that can take in telemetry data, flatten it, and load it dynamically. The suggested method fixes the problem of dealing with JSON payloads that have a lot of levels of nesting and changing schema needs without needing to do DDL manually. The pipeline automatically updates to new sensor settings by leveraging Snowpark's table functions to flatten JSON, pandas to mix data on the spot, and Snowflake's dynamic DDL features. In addition, this approach helps implement consistent data quality by standardizing column names and aligning with the target schema while delivering performance gain through efficient batch loading instead of row-by-row inserts. The transient staging and automated cleanup setup provide operational reliability this makes sure that data is always ready for downstream analytics in a form that is quick and correct.