Load GitLab data to Amazon Athena

GitLab is a DevOps platform that provides a REST API for managing projects, users, and CI/CD pipelines. Everything needed to build a working GitLab → Amazon Athena 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.

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Build your GitLab to Amazon Athena 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 GitLab 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.

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GitLab API at a glance

These values come from the GitLab API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the GitLab API?

Authentication can be performed by passing a token in the 'PRIVATE-TOKEN' header, which is the recommended method. Alternatively, tokens can be passed as a query parameter ('private_token') or via an 'Authorization' header for OAuth 2.0 tokens.

1. Get your credentials

1. Sign in to your GitLab instance. 2. In the upper-right corner, click your avatar. 3. Select Edit profile. 4. In the left sidebar, select Access > Personal access tokens. 5. Click Add new token (or follow the process to generate a new token). 6. Enter a token name, select the desired expiration date, and check the required scopes (e.g., 'api'). 7. Click Create personal access token and immediately copy the token value, as it cannot be viewed again once you leave the page.

2. Add them to .dlt/secrets.toml

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[sources.gitlab_source]
api_key = "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.

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What GitLab data can I load into Amazon Athena?

These are the GitLab endpoints dlt can load into Amazon Athena:

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How do I load only new GitLab records?

GitLab exposes updated_at on projects, 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.

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{"name": "projects", "endpoint": {
    "path": "projects",
    "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.

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What does the generated GitLab pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /projects/

/repository/commits and /groups//members from the GitLab API into Amazon Athena:

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

@dlt.source
def gitlab_source(private_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://gitlab.com/api/v4",
            "auth": {"type": "bearer", "token": private_token},
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "projects"}},
            {"name": "issues", "endpoint": {"path": "projects/:id/issues"}}
        ],
    }
    yield from rest_api_resources(config)


def load_gitlab_to_athena() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="gitlab_pipeline",
        destination="athena",
        staging="filesystem",
        dataset_name="gitlab_data",
    )
    load_info = pipeline.run(gitlab_source())
    print(load_info)


if __name__ == "__main__":
    load_gitlab_to_athena()

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

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How do I configure Amazon Athena as the destination?

Amazon Athena queries files in S3, 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 extra with uv add "dlt[athena]", then add the staging bucket and the Amazon Athena settings to .dlt/secrets.toml:

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[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.athena]
query_result_bucket = "s3://<query results bucket>"

[destination.athena.credentials]
aws_access_key_id = "<access key id>"
aws_secret_access_key = "<secret access key>"
region_name = "<region>"

Set athena_work_group under [destination.athena] to run the queries in a specific workgroup. See the dlt Amazon Athena destination docs.

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How do I query GitLab data in Amazon Athena?

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.

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import dlt

pipeline = dlt.pipeline("gitlab_pipeline")

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

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

# Step 3: Read the data
df = projects.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    projects         13
1      issues          3
"""

The columns are the fields the GitLab 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.

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How do I deploy the GitLab to Amazon Athena 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 GitLab loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load GitLab 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.

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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