Data Panda brings your GitHub repository, pull-request and workflow 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.
GitHub launched in 2008, founded by Tom Preston-Werner, Chris Wanstrath, PJ Hyett and Scott Chacon as a hosting service built around the Git version-control system. Microsoft acquired the company in October 2018 for 7.5 billion dollars in stock, and it has run as a Microsoft subsidiary since. The platform now reports more than 100 million developers and over 420 million repositories, with a product surface that has grown well beyond source-code hosting: GitHub Actions for CI/CD, GitHub Copilot for AI-assisted development, Issues and Projects for work tracking, Codespaces for cloud dev environments, Advanced Security for dependency and secret scanning, and Packages for artifact distribution.
For most engineering organisations GitHub is no longer just where the code lives. It is where pull requests get reviewed, where pipelines run, where bugs are filed and triaged, where dependency vulnerabilities show up, and increasingly where AI-suggested code lands in production. That is plenty of telemetry, and the built-in Insights and Security tabs cover the team-level view well. The harder questions live across GitHub and the systems around it: how PR cycle time per team tracks the deploy frequency the leadership team reports, how Copilot adoption maps to defect rates, which open vulnerabilities sit on services that touch customer data, and which repos have quietly gone unmaintained. Pulling the GitHub metadata into a warehouse is how those questions stop being a quarterly screenshot from the Org Insights page.
Pull-request flow, deploy frequency, Actions cost and security backlog in one place, across teams and repos.
Turn repository, PR and security events into the right work in the systems your teams already use.
Put PR, issue and Actions history behind AI that understands how your teams ship in practice.
Internal tools on GitHub metadata that engineering leads keep rebuilding as one-off scripts.
A list of concrete reports, automations and AI features we have built on GitHub data. Pick the one that matches your situation.
Where is PR cycle time slipping?
Median and 90th-percentile PR cycle time per team and repo, 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 vulnerabilities sit on services that touch customer data?
Open Dependabot and code-scanning alerts per repo, joined to the service tag and data classification you already track elsewhere. Critical alerts on a customer-facing payment service rank above critical alerts on an internal demo repo, instead of both arriving as the same red badge in the security tab.
Are our Copilot seats getting used?
Active versus inactive Copilot seats per team, with assignment date and last-active date. The finance team sees which seats to release before the next renewal, and engineering managers see whether adoption tracks the PR-throughput case the rollout was sold on.
GitHub spend per active developer and per active repo, broken out across Actions runner minutes, Copilot seats and Advanced Security. Renewal and seat-true-up conversations start with usage data instead of a flat invoice line in the SaaS-spend deck.
Customer-reported bugs that became GitHub issues, joined back to the CRM account. Account executives see whether the three promised fixes shipped between two deploy tags, before the renewal call rather than during it.
Cycle time, review time, deploy 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.
GitHub PRs that reference a Jira issue key push status updates back into the Jira issue: in review when the PR opens, in QA when it merges, done when the deploy tag 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.
Pull-request reviews, failed Actions runs on production deploys and new critical Dependabot alerts post into the team or on-call channel with repo, service and severity attached. Engineering leads spot review backlog and broken pipelines on the channel the team already watches, and security alerts on a customer-facing service surface seconds after the scan, rather than in tomorrow's digest mail.
Open Asana tasks in an engineering project tied to a GitHub PR pick up status from the PR: in progress when work starts, in review on PR open, complete on merge. Project managers and product leads see real progress on the Asana board, instead of pinging engineering to ask whether a ticket is still open or quietly shipped last Tuesday.
HubSpot tickets and deal notes tagged as a bug create GitHub issues in the right repo with customer tier, deal value and the reproduction notes attached. When engineering closes the issue and the deploy tag passes, the HubSpot record updates so account managers see the fix shipped without asking engineering for status the day before the renewal call.
These are the 8 tables we currently pull from GitHub into your warehouse. Query them directly in SQL, join them to the rest of your stack, or build reports on top.
Missing a table you need? We can extend the sync. Tell us what is missing and we will build it for you.
You keep the reporting tool you already have. We connect it to the warehouse where your GitHub data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
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.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
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.
The default pull is metadata: repos, branches, pull requests, reviews, issues, commits, Actions runs, releases and security alerts. 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.
Private repositories are visible to the connector only for the repos the GitHub App or token authorising the pull has been installed or granted access to. In practice the warehouse holds the metadata for the public repos in scope plus the private repos the integration is explicitly authorised for, which matches the boundary security teams ask for anyway. Org-level rollout typically starts narrow on a few teams and expands as code-owner mapping settles.
We review your GitHub setup and the systems around it. Together we pick the first thing worth building.