Mozilla Data Documentation
1.
Introduction
1.1.
What Data does Mozilla Collect?
1.2.
Tools for Data Analysis
1.3.
Terminology
2.
Tutorials & Cookbooks
2.1.
Getting Started
2.1.1.
Gaining Access
2.1.2.
Getting Help
2.1.3.
Reporting a problem
2.2.
Analysis
2.2.1.
Data Discovery Tools
2.2.1.1.
Using the Glean Dictionary
2.2.1.2.
Using the Probe Dictionary
2.2.2.
Data Modeling
2.2.2.1.
Where to store the data model assets
2.2.3.
Working with Looker
2.2.3.1.
Introduction to Looker
2.2.3.2.
Normalizing Country Data
2.2.3.3.
Normalizing Browser Version Data
2.2.3.4.
Using Growth and Usage Dashboards
2.2.3.5.
Using the Event Counts Explore
2.2.3.6.
Using the Funnel Analysis Explore
2.2.4.
Other Data Analysis Tools
2.2.4.1.
Introduction to GLAM
2.2.4.2.
Introduction to Operational Monitoring
2.2.4.3.
Introduction to STMO
2.2.5.
Accessing Public Data
2.2.6.
Accessing and working with BigQuery
2.2.6.1.
Access
2.2.6.2.
Writing Queries
2.2.6.3.
Optimization
2.2.6.4.
Accessing Desktop Data
2.2.6.5.
Accessing Glean Data
2.2.6.6.
Accessing Additional Properties
2.2.6.7.
Custom analysis with Spark
2.2.7.
Dataset-Specific
2.2.7.1.
Working with Normandy events
2.2.7.2.
Working with Crash Pings
2.2.7.3.
Working with Bit Patterns in Clients Last Seen
2.2.7.4.
Visualizing Percentiles of a Main Ping Exponential Histogram
2.2.8.
Real-time
2.2.8.1.
Seeing Your Own Pings
2.2.8.2.
See Real-time search metrics
2.2.9.
Metrics
2.2.9.1.
Daily Active Users (DAU and MAU)
2.2.9.2.
Active DAU (aDAU)
2.2.9.3.
Retention
2.3.
Operational
2.3.1.
Creating a Prototype Data Project on Google Cloud Platform
2.3.2.
Creating Static Dashboards with Protosaur
2.3.3.
Scheduling Queries
2.3.4.
Building and Deploying Containers to GCR with CircleCI
2.3.5.
Publishing Datasets
2.4.
Sending telemetry
2.4.1.
Implementing Experiments
2.4.2.
Sending Events
2.4.3.
Sending a Custom Ping
3.
Data Platform Reference
3.1.
Guiding Principles for Data Infrastructure
3.2.
Glean overview
3.3.
Overview of Mozilla's Data Pipeline
3.3.1.
HTTP Edge Server Specification
3.3.2.
Event Pipeline Detail
3.3.3.
Schemas
3.3.4.
Glean Data
3.3.5.
Channel Normalization
3.3.6.
Sampling
3.3.7.
Filtering
3.4.
Common Analysis Gotchas
3.5.
SQL Style Guide
3.6.
Airflow Gotcha's
3.7.
Telemetry Behavior Reference
3.7.1.
History of Telemetry
3.7.2.
Profile Behavior
3.7.2.1.
Profile Creation
3.7.2.2.
Real World Usage
3.7.2.3.
Profile History
3.7.3.
Engagement metrics
3.7.4.
User states/Segments
3.8.
Experimentation
3.9.
Metric Hub
3.10.
External data integration using Fivetran
3.11.
Project Glossary
4.
Dataset Reference
4.1.
Pings
4.2.
Derived Datasets
4.2.1.
Active Profiles
4.2.2.
Active Users Aggregates
4.2.3.
Addons
4.2.4.
Addons Daily
4.2.5.
Autonomous System Aggregates
4.2.6.
Clients Daily
4.2.7.
Clients Last Seen
4.2.8.
Events
4.2.9.
Events Daily
4.2.10.
Exact MAU
4.2.11.
Main Ping Tables
4.2.12.
Main Summary
4.2.13.
Socorro Crash Reports
4.2.14.
SSL Ratios (public)
4.2.15.
Telemetry Aggregates
4.2.16.
GLAM Aggregates
4.3.
Experiment Datasets
4.3.1.
Jetstream
4.3.2.
Accessing experiment data
4.3.3.
Accessing Heartbeat data
4.3.4.
Dynamic telemetry
4.3.5.
Experiment monitoring
4.4.
Search Datasets
4.4.1.
Search Aggregates
4.4.2.
Search Clients Engines Sources Daily
4.4.3.
Search Clients Last Seen
4.4.4.
Client LTV
4.5.
Non-Desktop Datasets
4.5.1.
Day 2-7 Activation
4.5.2.
Google Play Store
4.5.3.
Apple App Store
4.6.
Other Datasets
4.6.1.
hgpush
4.6.2.
Stub installer ping
4.6.3.
Activity Stream
4.6.4.
bmobugs
4.6.5.
Build metadata
4.6.6.
Release information
4.6.7.
Suggest
4.6.8.
Sponsored Tiles
4.7.
Firefox Accounts Datasets
4.7.1.
Firefox Account Attribution
4.7.2.
Firefox Account Funnel Metrics
4.7.3.
Firefox Account Email Metrics
4.8.
Static Datasets
4.8.1.
Normalized OS Names And Versions
5.
Historical Reference
5.1.
Previous AWS Pipeline Overview
5.2.
In-depth AWS Data Pipeline Detail
5.3.
Metrics
5.3.1.
Definitions
5.3.1.1.
Metrics
5.3.1.2.
Usage Criteria
5.3.1.3.
Slicing Dimensions
5.3.2.
Metrics Standardization and Policy
5.4.
Legacy Census Metrics
5.5.
Obsolete Datasets
5.5.1.
Attitudes Daily
5.5.2.
Churn
5.5.3.
Client Count
5.5.4.
Client Count Daily
5.5.5.
Crash Aggregates
5.5.6.
Crash Summary
5.5.7.
Error Aggregates
5.5.8.
First Shutdown Summary
5.5.9.
Heavy Users
5.5.10.
Legacy Mobile Datasets
5.5.11.
Longitudinal
5.5.12.
Retention
5.5.13.
Sync Summary
6.
Contributing
6.1.
Style Guide
6.2.
Structure
Light (default)
Rust
Coal
Navy
Ayu
Mozilla Data Documentation
Data Analysis Tools
This section covers data analysis tools that you can use for analyzing and visualizing data.