GitLab connector

Use your GitLab data for reporting, automation and AI.

Data Panda brings your GitLab project, merge-request, pipeline and security-scan data together with the data from the rest of your business. From one place, we turn it into dashboards, automations, AI workflows and custom apps your engineering leads, security and finance teams use every day.

About GitLab

The all-in-one DevSecOps platform self-managed teams reach for first.

GitLab started in 2011 as an open-source side project from Dmitriy Zaporozhets in Ukraine, and was incorporated as GitLab Inc in 2014. The company joined Y Combinator in 2015, ran fully remote from day one, and listed on the NASDAQ in October 2021 under the ticker GTLB. The product ships in three flavours: a free Community Edition that is fully open source, paid Premium and Ultimate tiers built on the Enterprise Edition, and GitLab Cloud as the SaaS option, with the same code base behind the self-managed install many customers run inside their own datacentre or VPC.

Where most peers cover one slice well, GitLab is positioned as a single application for the whole DevSecOps lifecycle. The same project that hosts the Git repo and merge requests also runs CI/CD pipelines, container and dependency scanning, secret detection, release management, a built-in container registry, package registry, issue tracking, wikis and value-stream analytics. That bundling is the reason it shows up so often in EU enterprises, public-sector and regulated organisations: one self-hostable platform replaces the typical GitHub plus Jenkins plus SonarQube plus Artifactory stack, with code, pipeline logs and scan findings staying inside the customer's own environment when policy demands it. The price of that breadth is real telemetry sprawl. Pipeline minutes, runner cost, MR cycle time per group, security findings per project and self-managed upgrade lag all live in different corners of the UI. Pulling the GitLab metadata into a warehouse is how those numbers stop being a quarterly screenshot from value-stream analytics.

What your GitLab data is for

What you get once GitLab is connected.

Engineering and DevSecOps reporting

Merge-request flow, pipeline cost, deployment frequency and security backlog in one place, across groups and projects.

MR cycle time and review time per group and project
Deployment frequency and lead time for changes per environment
Open SAST, DAST, dependency and secret-scan findings per project, weighted by service criticality

Process automation

Turn project, MR, pipeline and scan events into the right work in the systems your teams already use.

Open a Jira issue when a critical SAST finding lands on a service in scope for ISO 27001
Notify the on-call channel when a production deployment job fails twice in a row on the same environment
Auto-flag MRs that have been open longer than the team's agreed cycle-time target

AI workflows

Put MR, pipeline and scan history behind AI that understands how your groups ship in practice.

Defect-risk scoring on MRs based on author history, code-owner overlap and previous reverts on the same files
AI summaries of release scope from the MRs merged between two deploy tags or release tags
Triage assistant that routes new issues to the right project and CODEOWNERS entry

Custom apps on your data

Internal tools on GitLab metadata that engineering leads keep rebuilding as one-off scripts.

Engineering health workbench with cycle time, review time and deployment frequency per group
Security-finding triage console mapping open SAST and DAST findings to services and on-call owners
Pipeline-cost view with runner-minute spend per project and per pipeline pattern

Use cases

Use cases we deliver with GitLab data.

A list of concrete reports, automations and AI features we have built on GitLab data. Pick the one that matches your situation.

MR cycle timeTime from merge request open to merge, per group and project, with review time split out.

Review time per groupMedian time to first review and to approval, per reviewer pool.

Deployment frequencySuccessful production deployments per environment, per week.

Lead time for changesTime from commit to production deployment, per service.

Change-failure rateProduction deployments followed by a hotfix, rollback or revert, per service.

Pipeline-minute spendRunner minutes and runner cost per project and per pipeline pattern, per month.

SAST and DAST backlogOpen application-security findings per project, by severity, age and scanner.

Dependency findingsOpen dependency-scan findings per project, by severity and fix availability.

Project activity sprawlActive versus dormant projects per group, with last-commit and last-pipeline age.

Self-managed upgrade lagGitLab version per self-managed instance against the latest stable release.

Issue load per projectOpen and stale issues per project, by label and group.

Reopen rateMRs and issues reopened within N days, per group and project.

Real business questions

Answers you will finally get.

Where is MR cycle time slipping?

Median and 90th-percentile MR cycle time per group and project, with review time and merge-wait split out separately. When cycle time on a service has doubled over the last eight weeks, it surfaces as a number, together with the reviewer pool and reopen rate that usually move with it.

Which open SAST findings sit on services that touch customer data?

Open SAST, DAST and dependency findings per project, joined to the service tag and data classification you already track elsewhere. Critical findings on a customer-facing payment service rank above critical findings on an internal demo project, instead of both arriving as the same red badge in the security dashboard.

Where is the pipeline-minute budget going?

Runner-minutes and runner-cost per project, per pipeline pattern and per branch type. The finance team sees which three projects burn 60 percent of the monthly minutes, and engineering managers see whether nightly schedules and forked-MR pipelines explain the gap before the next runner-pool resize.

Value for everyone in the organisation

Where each function gets value.

For finance leaders

GitLab spend per active developer and per active project, broken out across runner minutes, Premium or Ultimate seats and self-managed infrastructure cost. Renewal and seat-true-up conversations start with usage data instead of a flat invoice line in the SaaS-spend deck.

For sales leaders

Customer-reported bugs that became GitLab issues, joined back to the CRM account. Account executives see whether the three promised fixes shipped between two release tags, before the renewal call rather than during it.

For operations

Cycle time, review time, deployment frequency, change-failure rate and security backlog in one view. Engineering leads, platform and security share the same numbers instead of three exports built the morning of the steerco.

Ideas

What you can automate with GitLab.

Pair with Jira

Keep Jira issues and GitLab MRs in sync

GitLab MRs that reference a Jira issue key push status updates back into the Jira issue: in review when the MR opens, in QA when it merges, done when the deployment job on the production environment passes. Engineering managers see the engineering-side flow on the Jira board the rest of the delivery org already lives in, instead of asking developers to update issue status by hand after every merge.

Pair with Slack

Route GitLab events to the right Slack channel

Merge-request reviews, failed pipeline jobs on production deployments and new critical SAST or dependency findings post into the team or on-call channel with project, environment and severity attached. Engineering leads spot review backlog and broken pipelines on the channel the team already watches, and security findings on a customer-facing service surface seconds after the scan, rather than in tomorrow's digest mail.

Pair with Confluence

Publish GitLab release notes into Confluence

Each release tag in GitLab generates a Confluence page with the MRs merged between the previous tag, the linked issue keys, the deployment status per environment and the open security findings on that release. Product, support and account managers read the same release context in the Confluence space they already use, instead of pinging engineering for what shipped on Tuesday.

Pair with Salesforce

Turn Salesforce-reported bugs into GitLab issues

Salesforce cases and opportunity notes tagged as a bug create GitLab issues in the right project with customer tier, ARR and the reproduction notes attached. When engineering closes the issue and the deployment job on the production environment passes, the Salesforce record updates so account executives see the fix shipped without asking engineering for status the day before the renewal call.

Your existing tools

Your data lands in a warehouse. Your BI tools read from it.

You keep the reporting tool you already have. We connect it to the warehouse where your GitLab data lives.

Power BI Microsoft

Fabric Microsoft

Snowflake Data warehouse

BigQuery Google

Tableau Visualisation

Excel Sheets & pivots

Three steps

From GitLab to answers in three steps.

Connect securely

OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.

Land in your warehouse

Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.

Reporting, automation, AI

We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.

Two ways to work with us

Pick the track that fits how you work.

Track 01

Self-serve

We set up the foundation. Your team builds on top.

GitLab connector configured and running
Warehouse set up in your cloud account
Clean access for your Power BI, Fabric or Tableau team
Documentation on what's in the data model
Sync monitoring so you're warned before reports break

Best fit Teams that already have a BI analyst or data engineer and want to own the build.

Track 02

Done for you

We build the whole thing, end to end.

Everything in Self-serve
Dashboards built to the questions your team actually asks
Automations between your systems
AI workflows scoped to real tasks your team runs
Custom apps where a dashboard does not cut it
Ongoing delivery at a pace that fits your team

Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.

Before you book

Frequently asked questions.

Who owns the data?

You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.

How fresh is the data?

Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.

Do I need a warehouse already?

No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.

Does the connector pull source code or just metadata?

The default pull is metadata: projects, branches, merge requests, reviews, issues, commits, pipelines, jobs, deployments, releases and security-scan results. The contents of source files are not part of the standard sync, which keeps the scope on the engineering-flow and security-posture reporting most teams want, rather than on code analytics. Pulling file contents needs a separate scoping conversation about IP, retention and access, and is not how we recommend most customers start.

Does this work for a self-managed GitLab instance?

Yes. The connector talks to the GitLab REST and GraphQL API on the URL you provide, so a self-managed instance running inside your own datacentre or VPC is treated the same way as GitLab Cloud. The auth token, the user mapping and the runner-cost source differ a little between the two, and the initial sync is scoped per group rather than per top-level org, but the warehouse model is the same.

GDPR-compliant

Data stays in the EU

You own the warehouse

A first deliverable live in four to six weeks.

We review your GitLab setup and the systems around it. Together we pick the first thing worth building.

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