Load Apache Spark data to DuckDB

Apache Spark provides REST APIs for monitoring application metrics and managing job submissions via dedicated server ports. Everything needed to build a working Apache Spark → 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 Apache Spark to DuckDB pipeline

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

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Apache Spark to DuckDB and run it on dltHub

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 Apache Spark 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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Apache Spark API at a glance

These values come from the Apache Spark API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Apache Spark API?

The REST API supports an optional 'Authorization' header using a 'Bearer' token scheme when 'JWSFilter' is configured on the Spark Master or UI. The token must be a JSON Web Token (JWT) signed with a shared secret key configured via Spark properties.

1. Get your credentials

Apache Spark does not feature a unified 'API key dashboard'. To authenticate with the Spark Master REST API, you must configure the Master server to use an authentication filter. Specifically, you need to enable the JWSFilter by setting the Spark configuration property 'spark.master.rest.filters=org.apache.spark.ui.JWSFilter' and defining a 'spark.org.apache.spark.ui.JWSFilter.param.secretKey' in your cluster's configuration files (e.g., spark-defaults.conf). You then generate a JSON Web Token (JWT) signed with this secret key to use as a Bearer token in the 'Authorization' HTTP header for API requests.

2. Add them to .dlt/secrets.toml

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[sources.apache_spark_source]
api_key = "your_jwt_bearer_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 Apache Spark data can I load into DuckDB?

These are the Apache Spark endpoints dlt can load into DuckDB:

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How do I load only new Apache Spark records?

Apache Spark exposes minDate on applications, 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": "applications", "endpoint": {
    "path": "applications",
    "incremental": {"cursor_path": "minDate", "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 Apache Spark pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v1/submissions/create and /api/v1 from the Apache Spark API into DuckDB:

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

@dlt.source
def apache_spark_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:4040/api/v1 or http://localhost:18080/api/v1 (history server) or http://localhost:6066/v1 (submission server)",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "applications", "endpoint": {"path": "applications"}},
            {"name": "task_list", "endpoint": {"path": "applications/{appId}/stages/{stageId}/{attemptId}/taskList"}}
        ],
    }
    yield from rest_api_resources(config)


def load_apache_spark_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="apache_spark_pipeline",
        destination="duckdb",
        dataset_name="apache_spark_data",
    )
    load_info = pipeline.run(apache_spark_source())
    print(load_info)


if __name__ == "__main__":
    load_apache_spark_to_duckdb()

Run it with python apache_spark_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 Apache Spark data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("apache_spark_pipeline").dataset()
df = data.applications.df()
print(df.head())

SQL:

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SELECT * FROM apache_spark_data.applications LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Apache Spark 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 Apache Spark 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 Apache Spark 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