endpoint, and assure that test suite does not fail when metric that you wish to remove is not included into test payload.
endpoint, and assure that test suite does not fail when metric that you wish to remove is not included into test payload.
1. Create an issue in the
[GitLab Data Team project](https://gitlab.com/gitlab-data/analytics/-/issues).
...
...
@@ -276,7 +276,7 @@ To remove a deprecated metric:
This step can be skipped if verification done during [deprecation process](#3-deprecate-a-metric)
reported that metric is not required by any data transformation in Snowflake data warehouse nor it is
used by any of SiSense dashboards.
1. After you verify the metric can be safely removed,
update the attributes of the metric's YAML definition:
...
...
@@ -1024,7 +1024,13 @@ On GitLab.com, we have DangerBot setup to monitor Product Intelligence related f
### 10. Verify your metric
On GitLab.com, the Product Intelligence team regularly monitors Usage Ping. They may alert you that your metrics need further optimization to run quicker and with greater success. You may also use the [Usage Ping QA dashboard](https://app.periscopedata.com/app/gitlab/632033/Usage-Ping-QA) to check how well your metric performs. The dashboard allows filtering by GitLab version, by "Self-managed" & "SaaS" and shows you how many failures have occurred for each metric. Whenever you notice a high failure rate, you may re-optimize your metric.
On GitLab.com, the Product Intelligence team regularly [monitors Usage Ping](https://gitlab.com/groups/gitlab-org/-/epics/6000).
They may alert you that your metrics need further optimization to run quicker and with greater success.
In [#g_product_intelligence](https://gitlab.slack.com/archives/CL3A7GFPF) Usage Ping JSON payload for
GitLab.com is shared every week.
You may also use the [Usage Ping QA dashboard](https://app.periscopedata.com/app/gitlab/632033/Usage-Ping-QA) to check how well your metric performs. The dashboard allows filtering by GitLab version, by "Self-managed" & "SaaS" and shows you how many failures have occurred for each metric. Whenever you notice a high failure rate, you may re-optimize your metric.