Databand you to define, capture, and monitor custom metrics for further insights into your data pipelines.
User-defined metrics can represent any quantitative measure of interest such as a business KPI or a data quality measurable - for example, counts, aggregations, summary statistics, or distribution measurements.
Databands Metric Tracking Database
With Databand's monitoring system, logging metrics reported by your tasks are automatically saved to Databand's metric tracking database. For more information, visit www.databand.ai or contact our team at [email protected]
To log metrics in Databand, use
log_metric() function which accepts two parameters:
metric_name - a string identifier for a metric
metric_value - the value for a metric during a given execution
Metric values can be both simple types (e.g., string, integer, bool) as well as complex types (e.g., lists or dicts).
Once defined, each execution of the pipeline will send the metric value to Databand, where you can perform time-series analysis of the metric and create alerts based on the value of a metric for a given pipeline execution.
Below is an example of
from dbnd import log_metric def add_values(a: int, b:int): sum = a + b log_metric('total', sum) return sum add_values(12, 16)
Updated about 1 month ago