Generate your anomaly test with Elementary AI
Let our Slack chatbot create the anomaly test you need.
elementary.freshness_anomalies
Monitors the freshness of your table over time, as the expected time between data updates.
Upon running the test, your data is split into time buckets (daily by default, configurable with the time bucket field),
and then we compute the maximum time without updates (in seconds) as the freshness value per bucket for the last training_period (by default 14 days).
The test then compares the freshness of each bucket within the detection period (last 2 days by default, controlled by the
detection_period var), and compares it to the freshness of the previous time buckets.
If there were any anomalies during the detection period, the test will fail.
Test configuration
Required configuration:timestamp_column
Default configuration: anomaly_direction: spike to alert only on delays.
tests:
— elementary.freshness_anomalies:
timestamp_column: column name
where_expression: sql expression
anomaly_sensitivity: int
detection_period:
period: [hour | day | week | month]
count: int
training_period:
period: [hour | day | week | month]
count: int
time_bucket:
period: [hour | day | week | month]
count: int
detection_delay:
period: [hour | day | week | month]
count: int
ignore_small_changes:
spike_failure_percent_threshold: int
drop_failure_percent_threshold: int
anomaly_exclude_metrics: [SQL expression]