TrendMiner tag “update” overwrites history instead of appending – scalability limitation We are currently using the trendminer‑interface SDK to create and populate custom tags (e.g. predictions / calculated KPIs). During implementation and testing, we have identified a major limitation that becomes critical at scale.When using: client.io.tag.save(tag_data_dict, index=False)to write data to an existing tag:Saving a new dataset overwrites existing historical data for that tag. It is not possible to append new time‑series data to an existing tag. Writing data for a new time range clears previously uploaded history.Although the documentation refers to this operation as an “update”, the actual behavior is a full overwrite of the time series, not an incremental append.Why this is a problem?Because appending is not supported, every update would require:Re‑uploading the entire historical dataset for each tag Maintaining full history externallyThis leads to:Significant performance and bandwidth overhead Long execution times High operational risk (partial uploads or failures can w
