Transparency in Coverage JSON format presents significant challenges

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The current Transparency in Coverage JSON format presents significant challenges when processing large, multi-gigabyte datasets. JSON, while flexible and human-readable, lacks inherent efficiencies for handling vast amounts of structured data at scale. Its nested and repetitive structure requires substantial computational resources, often demanding high memory usage that can quickly become cost-prohibitive. Processing such large datasets in their current format is not only resource-intensive but also time-consuming, as it necessitates extensive parsing, transformation, and optimization steps before meaningful analysis can be performed. These limitations make JSON an impractical choice for publishing and processing Transparency in Coverage data at scale.

An alternative would be to mandate NDJSON (Newline-Delimited JSON) – Streaming-Friendly JSON Alternative. Each line is a self-contained JSON object, making it easier to process line-by-line instead of loading the entire file. Provides better memory-efficient than regular JSON when handling massive datasets and can be easily processed using Unix tools (grep, awk), Python (pandas, jsonlines), or databases.