Reference production URLs for GUD and GLAM (#595)

src/concepts/glean/glean.md

@@ -32,7 +32,7 @@ Because Glean knows more about the individual data, such as its type and the ran
 
 **Provide a consistent base of telemetry**
 
-A baseline of analysis is important for all our products, from counting active users to retention and session times. This is supported out-of-the-box by the SDK, and funnels directly into visualization tools like the [Growth and Usage Dashboard (GUD)](https://growth-stage.bespoke.nonprod.dataops.mozgcp.net/).
+A baseline of analysis is important for all our products, from counting active users to retention and session times. This is supported out-of-the-box by the SDK, and funnels directly into visualization tools like the [Growth and Usage Dashboard (GUD)](https://gud.telemetry.mozilla.org/).
 
 Metrics that are common to all products, such as the operating system and architecture, are provided automatically in a consistent way.
 

src/cookbooks/clients_last_seen_bits.md

@@ -111,7 +111,7 @@ when we report a retention value for 2020-01-01, we're talking about what
 portion of clients active on 2020-01-01 are still active some number of days
 later.
 
-In particular, let's consider the "1-Week Retention" measure shown in [GUD](https://growth-stage.bespoke.nonprod.dataops.mozgcp.net/)
+In particular, let's consider the "1-Week Retention" measure shown in [GUD](https://gud.telemetry.mozilla.org/)
 which considers a window of 14 days.
 For each client active in "week 0" (days 0 through 6), we determine retention by
 checking if they were also active in "week 1" (days 7 through 13).
@@ -434,7 +434,7 @@ When we define forward-looking windows, however, we always choose a metric date
 some time in the past. How we number the individual bits depends on what
 metric date we choose.
 
-For example, in [GUD](https://growth-stage.bespoke.nonprod.dataops.mozgcp.net/), we show a "1-Week Retention" which considers a window of 14 days.
+For example, in [GUD](https://gud.telemetry.mozilla.org/), we show a "1-Week Retention" which considers a window of 14 days.
 For each client active in "week 0" (days 0 through 6), we determine retention by
 checking if they were also active in "week 1" (days 7 through 13).
 

src/cookbooks/main_ping_exponential_histograms.md

@@ -1,6 +1,6 @@
 # Visualizing Percentiles of a Main Ping Exponential Histogram
 
-[GLAM](https://glam-stage.bespoke.nonprod.dataops.mozgcp.net/) is great if you want to check out the behaviour of a histogram over a large population across a curated set of dimensions, but what if you have a follow-up question that doesn't fit into its UI model? This tutorial will go into the guts of how to reproduce a GLAM-like view using `sql.telemetry.mozilla.org` (STMO), along with some suggestions on how to dig deeper.
+[GLAM](https://glam.telemetry.mozilla.org/) is great if you want to check out the behaviour of a histogram over a large population across a curated set of dimensions, but what if you have a follow-up question that doesn't fit into its UI model? This tutorial will go into the guts of how to reproduce a GLAM-like view using `sql.telemetry.mozilla.org` (STMO), along with some suggestions on how to dig deeper.
 
 This tutorial tries to build up an understanding and intuition of how things work on a low-level before it gets to its main act of reproducing GLAM. If you don't care about the details, you can probably skip the earlier sections in this document.
 

src/cookbooks/retention.md

@@ -30,7 +30,7 @@ There are three standard methods for accessing retention metrics. These methods
 
 ### Mozilla Growth & Usage Dashboard (GUD)
 
-The [GUD](https://growth-stage.bespoke.nonprod.dataops.mozgcp.net/) provides plots and exportable tables of both retention metrics over time. Metrics are available for most products and can be sliced by OS, language, country, and channel.
+The [GUD](https://gud.telemetry.mozilla.org/) provides plots and exportable tables of both retention metrics over time. Metrics are available for most products and can be sliced by OS, language, country, and channel.
 
 ### Querying Smoot Usage Tables
 
@@ -104,7 +104,7 @@ When performing retention analysis it is important to understand that there are
 
 It is good practice to always compute confidence intervals for retention metrics, especially when looking at specific slices of users or when making comparisons between different groups.
 
-The [Growth and Usage Dashboard](https://growth-stage.bespoke.nonprod.dataops.mozgcp.net/) provides confidence intervals automatically using a jackknife resampling method over `client_id` buckets. This confidence intervals generated using this method should be considered the "standard". We show below how to compute them using the data sources described above. These methods use UDFs [defined in bigquery-etl](https://github.com/mozilla/bigquery-etl/blob/master/sql/moz-fx-data-shared-prod/udf_js/jackknife_ratio_ci/udf.sql).
+The [Growth and Usage Dashboard](https://gud.telemetry.mozilla.org/) provides confidence intervals automatically using a jackknife resampling method over `client_id` buckets. This confidence intervals generated using this method should be considered the "standard". We show below how to compute them using the data sources described above. These methods use UDFs [defined in bigquery-etl](https://github.com/mozilla/bigquery-etl/blob/master/sql/moz-fx-data-shared-prod/udf_js/jackknife_ratio_ci/udf.sql).
 
 We also note that it is fairly simple to calculate a confidence interval using any statistical method appropriate for proportions. The queries given above provide both numerators and denominators, so feel free to calculate confidence intervals in the manner you prefer. However, if you want to replicate the standard confidence intervals, please work from the example queries below.
 

src/datasets/bigquery/exact_mau/reference.md

@@ -18,7 +18,7 @@ aggregates across arbitrary slices of those dimensions.
 The tables follow a consistent methodology which is intended as a standard
 across Mozilla for MAU analysis going forward.
 
-Note that this data model is used in the [Growth & Usage Dashboard (GUD)](https://growth-stage.bespoke.nonprod.dataops.mozgcp.net/) and that some of this documentation is replicated in the [GUD documentation](https://mozilla.github.io/gud).
+Note that this data model is used in the [Growth & Usage Dashboard (GUD)](https://gud.telemetry.mozilla.org/) and that some of this documentation is replicated in the [GUD documentation](https://mozilla.github.io/gud).
 
 ## Table of Contents
 

src/tools/interfaces.md

@@ -10,11 +10,11 @@ These web-based tools do not require specialized technical knowledge (e.g. how t
 
 ### Mozilla Growth & Usage Dashboard (GUD)
 
-The [Mozilla Growth & Usage Dashboard](https://growth-stage.bespoke.nonprod.dataops.mozgcp.net/) (GUD) is a tool to visualize growth metrics in a standard way across Mozilla’s products. This is the first place you should look if you have a question like "how many people are using X?".
+The [Mozilla Growth & Usage Dashboard](https://gud.telemetry.mozilla.org/) (GUD) is a tool to visualize growth metrics in a standard way across Mozilla’s products. This is the first place you should look if you have a question like "how many people are using X?".
 
 ### Glean Aggregated Metrics Dashboard (GLAM)
 
-The [Glean Aggregated Metrics Dashboard](https://glam-stage.bespoke.nonprod.dataops.mozgcp.net/) (GLAM) is an interactive dashboard that is Mozilla’s primary self-service tool for examining the distributions of values of specific individual telemetry metrics, over time and across different user populations. It is similar to GUD in that it is meant to be usable by everyone; no specific data analysis or coding skills are needed. But while GUD is focused on a relatively small number of high level, derived product metrics about user engagement (e.g. MAU, DAU, retention, etc) GLAM is focused on a diverse and plentiful set of probes and data points that engineers capture in code and transmit back from Firefox and other Mozilla products.
+The [Glean Aggregated Metrics Dashboard](https://glam.telemetry.mozilla.org/) (GLAM) is an interactive dashboard that is Mozilla’s primary self-service tool for examining the distributions of values of specific individual telemetry metrics, over time and across different user populations. It is similar to GUD in that it is meant to be usable by everyone; no specific data analysis or coding skills are needed. But while GUD is focused on a relatively small number of high level, derived product metrics about user engagement (e.g. MAU, DAU, retention, etc) GLAM is focused on a diverse and plentiful set of probes and data points that engineers capture in code and transmit back from Firefox and other Mozilla products.
 
 As of this writing, GLAM is in active development but is already usable.