Load GitHub data to Dremio

GitHub REST API provides programmatic access to GitHub data including repositories, issues, pull requests, and user profile information. Everything needed to build a working GitHub → Dremio pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

---

Build your GitHub to Dremio pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the GitHub API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

---

GitHub API at a glance

These values come from the GitHub API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the GitHub API?

Authentication is performed by sending a token in the Authorization header using the 'Bearer' scheme (e.g., 'Authorization: Bearer '). Alternatively, basic authentication using a username and password is supported, but using a token is recommended.

1. Get your credentials

1. Navigate to your GitHub profile settings by clicking your profile photo in the top-right corner of any page and selecting Settings. 2. In the left sidebar, click Developer settings. 3. Under Personal access tokens, select either Fine-grained tokens or Tokens (classic). 4. Click Generate new token. 5. Provide a descriptive name, set an expiration date, and select the necessary repository access and permissions (scopes). 6. Click Generate token, then copy the token immediately, as it cannot be viewed again.

2. Add them to .dlt/secrets.toml

Copy
[sources.github_source]
github_api_key = "ghp_your_personal_access_token_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

---

What GitHub data can I load into Dremio?

These are the GitHub endpoints dlt can load into Dremio:

---

How do I load only new GitHub records?

GitHub exposes updated_at on user/repos, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

Copy
{"name": "repositories", "endpoint": {
    "path": "user/repos",
    "incremental": {"cursor_path": "updated_at", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

---

What does the generated GitHub pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /repos/{owner}/{repo}/issues and /user/repos from the GitHub API into Dremio:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def github_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.github.com",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "repositories", "endpoint": {"path": "user/repos"}},
            {"name": "issues", "endpoint": {"path": "repos/{owner}/{repo}/issues"}}
        ],
    }
    yield from rest_api_resources(config)


def load_github_to_dremio() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="github_pipeline",
        destination="dremio",
        staging="filesystem",
        dataset_name="github_data",
    )
    load_info = pipeline.run(github_source())
    print(load_info)


if __name__ == "__main__":
    load_github_to_dremio()

Run it with uv run python github_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

---

How do I configure Dremio as the destination?

Dremio loads from object storage, so dlt first writes the data to an S3 bucket through its filesystem staging destination; that is the staging="filesystem" in the code above. Install the extras with uv add "dlt[dremio,s3]", then add the staging bucket and the Dremio connection to .dlt/secrets.toml:

Copy
[destination.filesystem]
bucket_url = "s3://<staging bucket>"

[destination.filesystem.credentials]
aws_access_key_id = "<access key id>"
aws_secret_access_key = "<secret access key>"

[destination.dremio]
staging_data_source = "<object storage source>"

[destination.dremio.credentials]
host = "<host>"
port = 32010
username = "<user>"
password = "<password or personal access token>"
database = "<data source>"

staging_data_source is the Object Storage source in Dremio that points at the staging bucket, and database is the Dremio source dlt loads into. port is the Arrow Flight port; set drivername = "grpc+tls" for TLS. See the dlt Dremio destination docs.

---

How do I query GitHub data in Dremio?

After a run, pipeline.dataset() holds the loaded tables, one per resource. A Relation is a query on that dataset, either a whole table or a SQL string, and reading it gives you the data as a Pandas DataFrame, a PyArrow table or Python tuples. Nothing is read until you ask for it.

Copy
import dlt

pipeline = dlt.pipeline("github_pipeline")

# Step 1: Get the dataset from the pipeline
dataset = pipeline.dataset()

# Step 2: Get a Relation, either a whole table...
repositories = dataset.table("repositories")
# ...or any SQL query against Dremio
sample = dataset("SELECT * FROM repositories LIMIT 100")

# Step 3: Read the data
df = repositories.df()  # or .df(chunk_size=50) for a large table
arrow_table = sample.arrow()  # a PyArrow Table
rows = sample.fetchall()  # a list of Python tuples

# Which tables were loaded, and how big are they?
print(dataset.row_counts().df())
"""
     table_name  row_count
0  repositories         13
1        issues          3
"""

The columns are the fields the GitHub API returns. Your agent can run the same queries through the MCP server's execute_sql_query tool, and uv run dlthub show opens the Pipeline Dashboard with the schema, load metrics and a query box. See access loaded data in Python and exploring it in marimo notebooks.

---

How do I deploy the GitHub to Dremio pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw GitHub loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load GitHub data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

---

Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack