Load BinaryEdge data to DuckDB
Build a BinaryEdge to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the BinaryEdge API base URL, auth, endpoints, and incremental loading.
BinaryEdge is a threat intelligence and asset discovery service providing API access to security data and scanning results. Everything needed to build a working BinaryEdge → 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 BinaryEdge to DuckDB pipeline
Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.
PromptRunuvx dlthub-init@latestto build a pipeline from BinaryEdge 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 BinaryEdge 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.
BinaryEdge API at a glance
| Base URL | https://api.binaryedge.io/v2/ |
| Example endpoint | GET v2/query/search |
| Records found at | events |
| Authentication | all requests require an 'X-Key' header containing the API key — sent in the X-Key header |
| Pagination | Page-number up to 500 rows per page. BinaryEdge API V2 uses a numeric 'page' query parameter (default page=1, max page=500). The documentation result seen mentions pagesize context only indirectly; a wrapper code references 'pagesize' with a default of 100 when comparing totals, but exact REST parameter for page size (limit/per_page) and any next-page token/cursor are not specified in the provided sources. |
| Incremental field | page |
| API reference | https://docs.binaryedge.io/api-v2 |
These values come from the BinaryEdge API reference — the authoritative source if anything here looks out of date.
How do I authenticate with the BinaryEdge API?
Authentication is performed by passing a unique API key in the 'X-Key' HTTP header with every request.
1. Get your credentials
To obtain your API credentials for the BinaryEdge REST API:
- Log in to your BinaryEdge account dashboard at https://app.binaryedge.io.
- Navigate to the section labeled 'API Key', 'Developer Settings', or 'API Token' (this is typically located in your profile or account settings).
- If no key is present, select the option to generate a new API key.
- Copy the generated alphanumeric string to use in your API requests.
2. Add them to .dlt/secrets.toml
[sources.binaryedge_source] binaryedge_api_key = "your_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 BinaryEdge data can I load into DuckDB?
These are the BinaryEdge endpoints dlt can load into DuckDB:
| Resource | Endpoint | Method | Data selector | Description |
|---|---|---|---|---|
| host_search | v2/query/search | GET | Search events for given query | |
| domain_search | v2/query/domains/search | GET | Search domains | |
| ip_query | v2/query/ip/{target} | GET | Query specific IP | |
| dns_query | v2/query/domains/dns/{target} | GET | Query DNS for target | |
| torrent_search | v2/query/torrent/search | GET | Search torrents |
How do I load only new BinaryEdge records?
BinaryEdge exposes page on v2/query/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.
{"name": "host_search", "endpoint": { "path": "v2/query/search", "data_selector": "events", "incremental": {"cursor_path": "page", "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 BinaryEdge pipeline look like?
A standard dlt REST API pipeline — the same code you would write by hand, loading /v2/query/ip/ and /v2/user/subscription from the BinaryEdge API into DuckDB:
import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def binaryedge_source(api_key=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://api.binaryedge.io/v2/", "auth": {"type": "api_key", "api_key": api_key, "name": "X-Key", "location": "header"}, }, "resources": [ {"name": "host_search", "endpoint": {"path": "v2/query/search", "data_selector": "events"}}, {"name": "domain_search", "endpoint": {"path": "v2/query/domains/search", "data_selector": "domains"}} ], } yield from rest_api_resources(config) def load_binaryedge_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="binaryedge_pipeline", destination="duckdb", dataset_name="binaryedge_data", ) load_info = pipeline.run(binaryedge_source()) print(load_info) if __name__ == "__main__": load_binaryedge_to_duckdb()
Run it with python binaryedge_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 BinaryEdge 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):
import dlt data = dlt.pipeline("binaryedge_pipeline").dataset() df = data.host_search.df() print(df.head())
SQL:
SELECT * FROM binaryedge_data.host_search LIMIT 10;
See querying your data with dataset and exploring it in marimo notebooks.
How do I deploy the BinaryEdge 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 BinaryEdge loads into governed, documented models.
- Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.
What other destinations can I load BinaryEdge data to?
dlt loads into any of these — only the destination argument changes:
| Destination | Example value |
|---|---|
| PostgreSQL | "postgres" |
| BigQuery | "bigquery" |
| Snowflake | "snowflake" |
| Redshift | "redshift" |
| Databricks | "databricks" |
| Filesystem (S3, GCS, Azure) | "filesystem" |
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
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