Load Elasticsearch data to DuckDB

Elasticsearch REST API provides services to manage, configure, and access data and security features within an Elasticsearch cluster. Everything needed to build a working Elasticsearch → 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.

---

Build your Elasticsearch 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 Elasticsearch 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 Elasticsearch 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.

---

Elasticsearch API at a glance

These values come from the Elasticsearch API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Elasticsearch API?

Authentication is handled via the 'Authorization' header using schemes such as 'ApiKey', 'Basic', or 'Bearer'. 'ApiKey' requires a base64-encoded string of 'id:api_key', 'Basic' requires base64 of 'username:password', and 'Bearer' requires an access token.

1. Get your credentials

To generate an API key in Elasticsearch, use the Kibana dashboard or the REST API: 1. Kibana Dashboard: Navigate to the API Keys management page (search "API keys" in the global search bar), click 'Create API key', provide a name, and store the generated credentials (ID and API key) securely, as they will not be shown again. Alternatively, access it via the 'Help' menu (top right) -> 'Connection details' -> 'API key'. 2. REST API: Send a POST request to /_security/api_key. The request body can include a 'name' and optional 'expiration'. The response returns a JSON structure containing the 'id' and 'api_key' (base64-encoded). To use this key for authentication, send an 'Authorization' header with the value 'ApiKey <encoded_value>'.

2. Add them to .dlt/secrets.toml

Copy
[sources.elasticsearch_source]
api_key = "your_encoded_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.

---

What Elasticsearch data can I load into DuckDB?

These are the Elasticsearch endpoints dlt can load into DuckDB:

---

How do I load only new Elasticsearch records?

Elasticsearch exposes search_after on {index}/_search, 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": "search", "endpoint": {
    "path": "{index}/_search",
    "data_selector": "hits.hits",
    "incremental": {"cursor_path": "search_after", "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 Elasticsearch pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /_security/api_key and /_security/api_key/query from the Elasticsearch API into DuckDB:

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

@dlt.source
def elasticsearch_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://localhost:9200",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "search", "endpoint": {"path": "{index}/_search", "data_selector": "hits.hits"}},
            {"name": "nodes_info", "endpoint": {"path": "_nodes", "data_selector": "nodes"}}
        ],
    }
    yield from rest_api_resources(config)


def load_elasticsearch_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="elasticsearch_pipeline",
        destination="duckdb",
        dataset_name="elasticsearch_data",
    )
    load_info = pipeline.run(elasticsearch_source())
    print(load_info)


if __name__ == "__main__":
    load_elasticsearch_to_duckdb()

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

---

How do I query Elasticsearch 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):

Copy
import dlt

data = dlt.pipeline("elasticsearch_pipeline").dataset()
df = data.search.df()
print(df.head())

SQL:

Copy
SELECT * FROM elasticsearch_data.search LIMIT 10;

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

---

How do I deploy the Elasticsearch 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 Elasticsearch 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 Elasticsearch 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