You can scrape data.gov using AlterLab’s API. Use Python, Node.js, or cURL to fetch public pages. Extract structured data with Cortex and pay only for the tier that succeeds.

Why collect government data from Data.gov?

Public datasets on data.gov support many practical use cases. Market researchers track commodity price trends across federal reports. Data analysts build dashboards for public health metrics. Developers create tools that monitor infrastructure spending. Each use case benefits from timely, structured data that governments publish openly.

Technical challenges

Government portals like data.gov often include basic anti‑bot protections. They may check user‑agent strings, enforce rate limits, or require specific headers. Simple HTTP requests from a single IP frequently trigger blocks. To succeed you need rotating proxies, proper headers, and sometimes full JavaScript rendering. AlterLab’s Smart Rendering API handles these challenges automatically, letting you focus on the data.

Quick start with AlterLab API

Get started in minutes. Install the SDK, set your API key, and scrape a public page.

Python

import alterlab

client = alterlab.Client("YOUR_API_KEY")
response = client.scrape("https://data.gov/example-page")
print(response.text)

Node.js

import { AlterLab } from "alterlab";

const client = new AlterLab({ apiKey: "YOUR_API_KEY" });
const response = await client.scrape("https://data.gov/example-page");
console.log(response.text);

cURL

curl -X POST https://api.alterlab.io/v1/scrape \
  -H "X-API-Key: YOUR_KEY" \
  -d '{"url": "https://data.gov/example-page"}'

These examples assume you have read the Getting started guide. The API automatically selects the appropriate tier based on the target page. For data.gov, start at T1 — Curl for static HTML. If the request fails, the system escalates to T2 — HTTP, then T3 — Stealth, and so on. You only pay for the tier that returns a successful response.

Extracting structured data

Once you have the raw HTML you can pull out specific elements. Use CSS selectors that match the visible structure of the page.

import alterlab
from bs4 import BeautifulSoup

client = alterlab.Client("YOUR_API_KEY")
html = client.scrape("https://data.gov/example-page").text
soup = BeautifulSoup(html, "html.parser")
prices = [price.text for price in soup.select(".price-tag")]
print(prices)

import { AlterLab } from "alterlab";
import cheerio from "cheerio";

const client = new AlterLab({ apiKey: "YOUR_API_KEY" });
const html = await client.scrape("https://data.gov/example-page");
const $ = cheerio.load(html);
const prices = $(".price-tag").map((_, el) => $(el).text()).get();
console.log(prices);

Structured JSON extraction with Cortex

Cortex lets you define a schema and receive typed JSON without manual parsing.

import alterlab

client = alterlab.Client("YOUR_API_KEY")
result = client.extract(
    url="https://data.gov/example-page",
    schema={
        "type": "object",
        "properties": {
            "title": {"type": "string"},
            "price": {"type": "number"},
            "rating": {"type": "number"},
            "description": {"type": "string"}
        }
    }
)
print(result.data)  # Typed JSON output

The output matches the schema exactly, making downstream processing reliable.

Cost breakdown

AlterLab’s pricing is usage‑based. The table below shows the cost per request and per 1,000 requests for each tier. For data.gov, the typical path stays within T1‑T3.

You can view the full AlterLab pricing page for details. For data.gov, start at T1. The platform auto‑escalates if a lower tier fails, so you only pay for the tier that actually succeeds.

Best practices

Follow these rules to keep your scrapes compliant and efficient.

• Always check the site’s robots.txt before scraping.
• Respect rate limits; a safe default is one request per second per IP.
• Use proper headers that mimic a real browser.
• Monitor response codes; back off if you receive 429 or 5xx errors.
• Store results locally before forwarding them to downstream systems.

Scaling up

When you need to collect large volumes of data, batch your requests and schedule them during off‑peak hours. Use AlterLab’s scheduling feature to run recurring scrapes without manual intervention. For massive datasets, consider splitting the work across multiple tiers and regions to reduce latency.

Key takeaways

Scraping data.gov is straightforward when you use a capable API. Start with static HTML using T1, let the system promote tiers as needed, and extract structured data with Cortex. Keep your usage within rate limits, respect robots.txt, and monitor costs through the pricing page. With these steps you can build reliable pipelines that deliver public government data to your applications.