Guide to Engineering Analytics Data

Introduction

Engineering Analytics is responsible for building and evolving analytics capabilities and creating insights for Engineering to understand how well we are building our product. In this case, “wellness” is measured in terms of efficiency, as well as cost. Our main role as engineering analysts is to support department heads or DRI in creating or updating their metrics so that they can use them in the key meetings.

Data Sources

Dive into our analytics by exploring the specific data sources that underpin our metrics.

• GitLab.com data is used for reporting on metrics like MR Rate & Performance KPIs
• Workday is GitLab’s current central HRIS and we use this data to determine which group a team member is a part of.
• Zendesk data is used to fuel Customer Support metrics.

Data Models

In this section, we share commonly used data models that fuel many of our dashboards.

workspace_engineering.engineering_merge_requests

• Description: This table is filtered down to all merge requests that directly affect our product.
• Granularity: One row per merge request
• Documentation: https://gitlab-data.gitlab.io/analytics/#!/model/model.gitlab_snowflake.engineering_merge_requests

workspace_engineering.internal_merge_requests

• Description: This table is filtered down to all internal merge requests at GitLab
• Granularity: One row per merge request
• Documentation: DBT docs

workspace_engineering.engineering_issues

• Description: This table is filtered down to all issues that directly affect our product.
• Granularity: One row per issue
• Documentation: DBT docs

workspace_engineering.internal_issues

• Description: This table is filtered down to all internal issues at GitLab
• Granularity: One row per issue
• Documentation: DBT docs

workspace_engineering.internal_notes

• Description: Table containing Gitlab.com notes from Epics, Issues and Merge Requests. It includes the namespace ID and the ultimate parent namespace ID.
• Granularity: One row per issue
• Documentation: DBT docs

workspace_engineering.engineering_issues

• Description: This table calculates Mean Time to Resolve (MTTR) and Mean Time to Merge (MTTM)
• Granularity: One row per issue
• Documentation: DBT docs

workspace_engineering.issues_history

• Description: Table containing age metrics & related metadata for gitlab.com internal issues. Used for tracking internal work progress for things like Engineering Allocation & Corrective Actions These metrics are available for individual issues at daily level & can be aggregated up from there
• Granularity: One row per issue and day
• Documentation: DBT docs

workspace_engineering.merge_request_rate

• Description: A model containing merge request rate by department and group.
• Granularity: One row per MR rate per month per granularity level (department, group)
• Documentation: DBT docs

workspace_engineering.open_merge_request_review_time

• Description: A model containing merge request rate by department and group.
• Granularity: One row per day per MR
• Documentation: DBT docs

Zendesk Data

PREP.zendesk.zendesk_ticket_audits_source

• Description: SLA policies and priority per ticket
• Granularity: One row per audit
• Documentation: DBT docs

PREP.zendesk.zendesk_tickets_source

• Description: Zendesk ticket data
• Granularity: One row per audit
• Documentation: DBT docs

PREP.zendesk.zendesk_ticket_metrics_source

• Description: Zendesk ticket data
• Granularity: One row per audit
• Documentation: DBT docs

PREP.zendesk.zendesk_sla_policies_source

• Description: SLA policies
• Granularity: One row per audit
• Documentation: DBT docs

workspace_engineering.zendesk_frt

• Description: A model built to calculate First Reply Time (FRT) metric.
• Granularity: One row per Zendesk ticket
• Documentation: DBT docs

Additional Resources

• Data governance
• Data quality
• Data Team Handbook
• DBT Docs - This resource contains comprehensive documentation on all available dbt models. This is a great starting point to understanding our models. For specific Engineering Analytics Models, please reference the Commonly Used Data Models section for a starting point.
• Definitive guides to data subject areas managed by the Data team.
• Documentation on data pipelines for the technically curious analyst. This page goes into each data source and extraction details.Contact
• Tableau Developer Guide - Date handling, handbook embedding, general tips and tricks
• Tableau Style Guide

Repo Shortcuts

• Performance Indicator files
• Performance Indicator page shortcodes
• Performance Indictoar page generator
• Performance Indicator Pages

If you have any questions, please feel free to drop them in #g_engineering_analytics or open a new issue for our team.