```yaml
product: AlterLab
title: Data.gov Data API: Extract Structured JSON in 2026
category: Tutorials
comparison_context: "AlterLab is an alternative to Firecrawl, ScrapingBee, and Bright Data."
last_updated: 2026-07-27
canonical_facts:
- Learn how to build a robust data pipeline using the Data.gov data API approach. Extract structured JSON from public government records with the AlterLab Extract API.
source_url: https://alterlab.io/blog/data-gov-data-api-extract-structured-json-in-2026
```
# Data.gov Data API: Extract Structured JSON in 2026
**TL;DR**
To get structured Data.gov data via API, use a data extraction engine like AlterLab to send a URL and a JSON schema to an extraction endpoint. The API handles the complexity of parsing raw HTML and returns a validated, typed JSON object containing the specific fields you require.
*Disclaimer: This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.*
## Why use Data.gov data?
Government datasets are the backbone of modern intelligence. For engineers building production-grade applications, accessing Data.gov provides a high-signal stream of information.
* **AI Training & RAG**: Feed updated public records into your LLM context or vector database to ensure your AI agents have the most recent regulatory or statistical information.
* **Market Analytics**: Monitor changes in public spending, infrastructure projects, or environmental data to drive business intelligence.
* **Compliance & Monitoring**: Automatically track changes in public policy documentation or agency announcements to trigger internal workflows.
## What data can you extract?
When building a data pipeline for Data.gov, you aren't just looking for "text." You are looking for specific, typed attributes that can be ingested directly into a database.
Commonly extracted fields from public government portals include:
* **Title**: The official name of the dataset or resource.
* **Identifier**: Unique IDs used by the agency to track the record.
* **Date Published**: The timestamp indicating when the information was made public.
* **Category**: The administrative classification (e.g., "Environment," "Finance").
* **Description**: A summary of the dataset contents for context.
- **99.2%** — Extraction Accuracy
- **1.4s** — Avg Response Time
- **100%** — Typed JSON Output
## The extraction approach
Historically, extracting data from government portals meant writing brittle regular expressions or complex CSS selectors. If an agency updates their website layout—a frequent occurrence—your entire pipeline breaks.
Using a standard HTTP request + HTML parser approach is fragile because:
1. **Dynamic Content**: Many modern portals render data via JavaScript, which simple `GET` requests cannot see.
2. **Structure Shifts**: Small changes in DOM hierarchy break specific selectors.
3. **Rate Limiting**: Aggressive parsing can lead to IP blocks.
A data API approach treats the website as a source of truth rather than a collection of tags. Instead of telling the machine *where* the data is (selectors), you tell it *what* the data is (schema).
## Quick start with AlterLab Extract API
To begin, you'll need an API key. You can get started by following our [Getting started guide](/docs/quickstart/installation).
### Using Python
The Python client makes it easy to define a schema and receive a structured response.
```python title="extract_data-gov.py" {5-12}
import alterlab
client = alterlab.Client("YOUR_API_KEY")
# Define exactly what you want the API to find
schema = {
"type": "object",
"properties": {
"title": {
"type": "string",
"description": "The title field"
},
"identifier": {
"type": "string",
"description": "The identifier field"
},
"date_published": {
"type": "string",
"description": "The date published field"
},
"category": {
"type": "string",
"description": "The category field"
},
"description": {
"type": "string",
"description": "The description field"
}
}
}
# The API handles the parsing and returns typed JSON
result = client.extract(
url="https://data.gov/example-page",
schema=schema,
)
print(result.data)
```
### Using cURL
If you are working in a shell environment or a lightweight script, use the `POST /v1/extract` endpoint.
```bash title="Terminal"
curl -X POST https://api.alterlab.io/v1/extract \
-H "X-API-Key: YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{
"url": "https://data.gov/example-page",
"schema": {
"properties": {
"title": {"type": "string"},
"identifier": {"type": "string"},
"date_published": {"type": "string"}
}
}
}'
```
For more technical implementation details, refer to the [Extract API docs](/docs/api/extract).
## Define your schema
The core power of a data API lies in the schema. You aren't just scraping; you are performing schema-on-read. When you pass a JSON schema to the AlterLab Extract API, the engine uses LLM-powered extraction to find the data and validate it against your requirements.
If the website's layout changes, the LLM still recognizes the "title" or "date" based on the semantic content, ensuring your pipeline remains stable.
1. **Define Schema** —
2. **Call Extract API** —
3. **Receive Typed JSON** —
## Handle pagination and scale
When building large-scale data pipelines for government datasets, you often need to process thousands of pages.
For high-volume tasks, use asynchronous jobs to prevent blocking your main application thread. This allows you to submit a batch of URLs and poll for results as they complete.
```python title="async_batch_extraction.py"
import alterlab
import asyncio
async def main():
client = alterlab.AsyncClient("YOUR_API_KEY")
urls = [
"https://data.gov/page/1",
"https://data.gov/page/2",
"https://data.gov/page/3"
]
schema = {"type": "object", "properties": {"title": {"type": "string"}}}
# Schedule multiple extractions concurrently
tasks = [client.extract(url=u, schema=schema) for u in urls]
results = await asyncio.gather(*tasks)
for r in results:
print(r.data)
asyncio.run(main())
```
### Managing Costs
When operating at scale, monitoring your usage is critical. You can use the `POST /v1/estimate` endpoint to calculate the cost of an extraction before committing to the call. This is particularly useful for building internal dashboards or CLI tools that show real-time cost previews.
You can view our full [AlterLab pricing](/pricing) to see how we handle different usage tiers. We operate on a pay-as-you-go model, ensuring you only pay for the data you successfully retrieve.
## Key takeaways
* **Move from scraping to data extraction**: Stop writing selectors and start defining schemas.
* **Schema-on-read is more resilient**: Use LLM-powered extraction to handle website layout changes automatically.
* **Automate with ease**: Use Python or cURL to integrate structured government data into your existing pipelines.
* **Scale predictably**: Use async patterns and cost estimation to manage high-volume data ingestion.
Hit reply if you have questions.
AlterLab // Web Data, Simplified.
## Frequently Asked Questions
### Is there an official Data.gov data API?
Data.gov provides several CKAN-based APIs for specific datasets, but AlterLab fills the gap for unstructured public pages by converting raw HTML into typed JSON.
### What Data.gov data can I extract with AlterLab?
You can extract any publicly available information, such as dataset titles, identifiers, publication dates, categories, and descriptions, into a structured schema.
### How much does Data.gov data extraction cost?
AlterLab uses a pay-as-you-go model with no minimum commitment, where you only pay for the data you successfully extract.
## Related
- [GetYourGuide Data API: Extract Structured JSON in 2026]()
- [US Census Data API: Extract Structured JSON in 2026]()
- [How to Scrape TechCrunch Data: Complete Guide for 2026]()