elementary.upload_run_results
from each invocation that
produces a result object.
elementary.upload_dbt_invocation
.
It also contains information about your job or what triggered the invocation such as pull_request_id
, git_sha
, or cause
.
If you’re using an orchestrator that Elementary natively supports such as dbt Cloud or GitHub Actions,
this data is automatically populated, otherwise, you can populate it by using environment variables in the form of DBT_<COLUMN_NAME>
.
For instance, adding DBT_JOB_NAME
will populate dbt_invocations.job_name
with the value of the environment variable.
dbt_run_results
and dbt_models
.
dbt_run_results
and dbt_snapshots
.
dbt_invocations
.
elementary.handle_tests_results
.
handle_test_results
that is executed at the end of dbt
test invocations.
data_monitoring_metrics
that runs the same query the anomaly detection tests run to calculate
anomaly scores.
The purpose of this view is to provide visibility to the results of anomaly detection tests.
metrics_anomaly_score
that calculates if values of metrics from the latest runs would have been
considered anomalies in different anomaly scores.
This can help you decide if there is a need to adjust the anomaly_score_threshold
.
data_monitoring_metrics
that is used to determine when a specific anomaly detection test was last
executed.
Each anomaly detection test queries this view to decide on a start time for collecting metrics.