# GitHub Connector: Integration with BigQuery, Redshift, Snowflake, and more without manual pipelines > Managed connector to sync GitHub repos, issues, pull requests, and commits to BigQuery, Redshift, Snowflake, and more. No maintenance. Source: https://www.erathos.com/en/blog/github-launch-connector Em português: https://www.erathos.com/blog/github-launch-connector Published: 2026-05-12 Category: New Connectors ![](https://cms-media.erathos.com/NXtvdOKoawOKiInlHCW6dgyHBPU-1.png) Bring repositories, members, issues, pull\_requests, and commits into your warehouse without worrying about pagination, rate limits, alerts, and schema evolution. GitHub exposes a well-documented REST API, making it seem trivial to pull PRs, commits, and issues directly into your warehouse with a custom script. In practice, this becomes a [custom ETL](/blog/etl-vs-elt-principais-diferencas) pipeline that someone has to maintain forever, dealing with pagination, rate limits shared across operations, and schemas that change without warning. The Erathos connector delivers this integration as a managed service, without requiring your team to write or maintain any code. ### The problem with custom-built GitHub pipelines Every data team, at some point, is asked the same question: how are we measuring engineering productivity? The answer almost always lies in the data that already lives on GitHub: pull requests, commits, issues, and org members. What seems like a simple ELT use case actually hides a classic SaaS API ingestion problem. The pipeline works until the day it stops, and nobody notices. Building from scratch is tempting. The GitHub API is public, well-documented, and any engineer can pull a list of PRs in just a few lines. The cost comes later, and it's not in the code you write in the first sprint. It's in everything you have to maintain forever. Each endpoint relevant to analytics paginates differently, and the logic you wrote once needs to be revisited when GitHub changes a default. The rate limit is shared across operations and drops without warning when you scale to a large org, so you end up implementing exponential backoff, ETag caching, and some mechanism to distribute the extraction window. Schemas change: new fields appear, others become nullable. Your Airflow DAG keeps reporting success while delivering less data than it should. And there's the part nobody remembers when estimating: alerting. When a run fails, someone needs to know. When the record count drops by 80% compared to the previous week, someone needs to know. When the PR count hits zero for an active repository, someone needs to know. Building and maintaining this observability logic (not for the product, but for the pipeline itself) is a platform project in its own right. The worst-case scenario isn't a pipeline that breaks. It's a pipeline that silently degrades, and the metric going to the CTO's dashboard is already wrong. ### What you can do with GitHub data in your warehouse Before talking about the connector, it's worth noting what you gain when this data lands fully modeled alongside the rest of your modern data stack. **Engineering analytics and DORA metrics.** Pull request lead time (from `opened_at` to `merged_at`), throughput by repository, average review time, and distribution of changes among authors. Combining this with CI/CD deployment data, you close the loop on the four DORA metrics (deployment frequency, lead time for changes, change failure rate, and MTTR) without relying on an external tool that charges per license. **Identifying review bottlenecks.** Crossing `pull_requests` with `members`, you quickly see who is sustaining the review queue and where changes stall the longest. This is a management insight that the data team could never deliver when the data only lived inside the SaaS tool. **Bug and quality hotspots.** Combining `issues` with `commits` per repository, you identify where churn is highest and where issue resolution time is longest. This is the kind of analysis that previously required an analyst digging through manual reports. **Auditing, compliance, and onboarding.** Knowing who joined and left the org, who has access to which repositories, and joining this with your HR system inside the warehouse. Highly useful for teams that need to demonstrate controls for SOC 2 or ISO 27001. **Combining with the rest of your stack.** The real value appears when GitHub data lives next to Linear or Jira, your CRM, and product data. That's when questions like "how much engineering time went to enterprise clients last quarter?" stop being a spreadsheet exercise and become a dbt model maintained by the team. It's also at this point that organizing data into layers, as proposed by the [medallion architecture](/blog/arquitetura-medalhao-guiacompleto), makes all the difference: raw repositories and pull\_requests in Bronze, validated and enriched data in Silver, and production-ready DORA metrics for executives in Gold. ### Why outsource ingestion to Erathos The premise of the connector is simple: managing ingestion pipelines shouldn't be your data team's responsibility. Pagination, rate limits, retries, schema evolution, failure alerts, degradation alerts, backfills. All of this is the ingestion platform's job to solve, not a technical decision for your analytics engineer. The same reasoning applies to [companies without a dedicated data team](/blog/centralizacao-de-dados-sem-time-dedicado): separating who connects from who maintains is what decides whether the integration scales or becomes a recurring headache. That is exactly what the connector delivers. You generate the token, connect the org, and from that point on: **End-to-end visibility of every execution.** How long each extraction took, how many records came per endpoint, which windows were processed, and where retries occurred. This is invaluable both for finding the root cause of an anomalous metric and for answering product teams who want to know why a number changed. **Out-of-the-box alerting.** Failures, volume drops, and window delays are detected and sent through the alerting integrations your team already uses. You don't have to write this code. **Supported re-processing.** When you need to re-run a specific window, whether because your modeling changed or because you received a data correction from the source, it's a button click, not an ad-hoc SQL exercise. On top of that, proper pagination, rate limit management, schema evolution, and backfills are handled by the platform. The team focuses on the data model, not the plumbing. ### What's available in the connector The connector delivers five endpoints ready to be materialized in your destination warehouse: Endpoint What it contains `repositories` The org's repository catalog `members` Org members `issues` Open and closed issues `pull_requests` Pull requests with review and merge metadata `commits` Commits from synced repositories Supported destinations are **BigQuery**, **Redshift**, **PostgreSQL**, **ClickHouse**, **Supabase**, **Azure Synapse**, **Snowflake**, and **Amazon S3**. Authentication is done using a GitHub Personal Access Token (classic). The token needs two scopes: `repo` (to read repository data) and `read:org` (to read org members). The step-by-step process is described in the [connector documentation](https://docs.erathos.com/connectors/apis/github). ### Frequently asked questions about the GitHub connector **Do I need admin access to the org to connect GitHub?** You need to generate a Personal Access Token (classic) with `repo` and `read:org` scopes. Depending on your user permissions in the org, this might require approval from an admin, but the connection itself does not require the configuring user to be an admin. **Does the connector sync multiple repositories at once?** Yes. The connector operates at the org level, so all repositories accessible by the token are synced without needing to configure them one by one. **Does the GitHub API rate limit affect syncing?** Not on your end. GitHub's rate limit is shared across operations and drops without warning in large orgs. The connector handles this automatically via exponential backoff and extraction window distribution. **Can I sync more than one GitHub org?** Yes, simply set up one connection per org, each with its own token. **Which destinations can the GitHub connector sync data to?** BigQuery, Redshift, PostgreSQL, ClickHouse, Supabase, Azure Synapse, Snowflake, and Amazon S3. ### Available Pipelines The GitHub connector is available with the following destinations: - [GitHub → BigQuery](/pipelines/github-bigquery) - [GitHub → Redshift](/pipelines/github-redshift) - [GitHub → PostgreSQL](/pipelines/github-postgresql) - [GitHub → ClickHouse](/pipelines/github-clickhouse) - [GitHub → Supabase](/pipelines/github-supabase) - [GitHub → Azure Synapse](/pipelines/github-azure-synapse) - [GitHub → Snowflake](/pipelines/github-snowflake) - [GitHub → Amazon S3](/pipelines/github-amazon-s3) ### Get started [Create your Erathos account](https://app.erathos.com/signup?slug=blog&button=cta&utm_campaign=github_release) and test the connector with your repositories. Within a few minutes, using your token and org name, you'll see the first data landing in your warehouse, with no pipeline code to write, maintain, or monitor. Engineering generates data every day. It makes little sense for this data to remain locked inside a SaaS tool, outside your models, or worse, in a home-grown pipeline that will cost you engineering cycles every single month.