Note that while STMO is not yet considered deprecated, Looker is the preferred solution for producing data visualizations and dashboards at Mozilla (where possible). You can learn more about how to use it in Introduction to STMO. Requires (surprise!) familiarity with SQL, and for your data to be explicitly exposed as an STMO data source. Is an instance of the very fine Redash software, allowingįor SQL-based exploratory analysis and visualization / dashboard These tools require more specialized knowledge to use. 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.įor more information on how to use GLAM, see Introduction to GLAM. But while GUD is focused on a relatively small number of high level, derived product metrics about user engagement (e.g. It is similar to GUD in that it is meant to be usable by everyone no specific data analysis or coding skills are needed. The Glean Aggregated Metrics Dashboard (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. Glean Aggregated Metrics Dashboard (GLAM) 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 (GUD) is a tool to visualize growth metrics in a standard way across Mozilla’s products. It allows data exploration and visualization by experts and non-experts alike.įor a brief introduction to Looker, see Introduction to Looker. In 2020, Mozilla chose Looker as its primary tool for analyzing data. how to write an SQL query, deep knowledge of BigQuery). These web-based tools do not require specialized technical knowledge (e.g. Glean Aggregated Metrics Dashboard (GLAM).There are different tools available, each with their own strengths, tailored to a variety of use cases and skill sets.

This is a starting point for making sense of the tools used for analyzing Mozilla data. Guiding Principles for Data Infrastructure Building and Deploying Containers to GCR with CircleCI Creating Static Dashboards with Protosaur Creating a Prototype Data Project on Google Cloud Platform Visualizing Percentiles of a Main Ping Exponential Histogram Working with Bit Patterns in Clients Last Seen

Tools for data analysis