Load Roblox Cloud data to DuckDB

Roblox Open Cloud provides a set of REST APIs for managing game resources and automation tasks through API keys or OAuth 2.0 authentication. Everything needed to build a working Roblox Cloud → DuckDB 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 Roblox Cloud to DuckDB 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 Roblox Cloud 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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Roblox Cloud API at a glance

These values come from the Roblox Cloud API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Roblox Cloud API?

Open Cloud APIs support API keys (via the x-api-key header) or OAuth 2.0 (via an Authorization: Bearer token header). Legacy APIs use cookie-based authentication, which is not recommended for production.

1. Get your credentials

1. Navigate to the Roblox Creator Dashboard (https://create.roblox.com/dashboard/credentials).
2. Open the API Keys tab and click "Create API Key".
3. Provide a name and select the specific API systems (e.g., Data Stores, Messaging Service) and operations (read/write) required.
4. Define allowed IP addresses in the Security section for better protection.
5. Click "Save & Generate Key", and copy the generated key immediately as it will not be displayed again.

2. Add them to .dlt/secrets.toml

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[sources.roblox_cloud_source]
api_key = "your_secret_api_key_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 Roblox Cloud data can I load into DuckDB?

These are the Roblox Cloud endpoints dlt can load into DuckDB:

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How do I load only new Roblox Cloud records?

Roblox Cloud exposes pageToken on cloud/v2/users/{user_id}/inventory-items, 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": "inventory_items", "endpoint": {
    "path": "cloud/v2/users/{user_id}/inventory-items",
    "data_selector": "inventoryItems",
    "incremental": {"cursor_path": "pageToken", "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 Roblox Cloud pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading universes and datastores from the Roblox Cloud API into DuckDB:

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

@dlt.source
def roblox_cloud_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://apis.roblox.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "inventory_items", "endpoint": {"path": "cloud/v2/users/{user_id}/inventory-items", "data_selector": "inventoryItems"}},
            {"name": "data_stores", "endpoint": {"path": "cloud/v2/universes/{universe_id}/data-stores", "data_selector": "dataStores"}}
        ],
    }
    yield from rest_api_resources(config)


def load_roblox_cloud_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="roblox_cloud_pipeline",
        destination="duckdb",
        dataset_name="roblox_cloud_data",
    )
    load_info = pipeline.run(roblox_cloud_source())
    print(load_info)


if __name__ == "__main__":
    load_roblox_cloud_to_duckdb()

Run it with uv run python roblox_cloud_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 query Roblox Cloud data in DuckDB?

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("roblox_cloud_pipeline")

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

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

# Step 3: Read the data
df = inventory_items.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  inventory_items         13
1      data_stores          3
"""

The columns are the fields the Roblox Cloud 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 Roblox Cloud to DuckDB 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 Roblox Cloud 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 Roblox Cloud 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