##  Automated Alert Mechanisms
We've established automated systems in Snowflake to identify significant data changes at both the regional and workspace levels, enhancing data quality and reliability. Additionally, we’ve set up a webhook as a backup in case Snowpipe, a data ingestion service provided by Snowflake, fails.
Our primary focus is identifying the following:
  * Daily row count deviations: Monitoring the number of rows for each data source and table in the last 7 days.
  * Percentage change alerts: If there is a decrease of more than 25% in the row count from the previous day, an alert is triggered. The 25% threshold for deviations is set to balance sensitivity and practicality in our data monitoring system. By establishing this threshold, we aim to identify significant anomalies without overloading the system with alerts for minor fluctuations that are within the normal range of data variability. Deviations under 25% are considered acceptable as they typically reflect natural variations in data volume rather than issues needing immediate attention. This approach allows us to focus our resources on addressing more substantial and potentially problematic changes, ensuring efficient and effective data quality management.
  * Weekend exception: To avoid false positives due to typically lower weekend activity, no alerts are generated for data collected on weekends (Saturdays and Sundays, CEST time zone).
