We extract dataset metadata, agency catalogues, resource URLs, and update histories from data.gov. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Dataset Metadata objects from data.gov. All fields typed and schema-versioned.
"dataset_id": "gov-usda-12345", "title": "National Agricultural Statistics", "organization_name": "Department of Agriculture", "metadata_modified": "2026-04-12T08:00:00Z", "license_title": "U.S. Government Work", "update_frequency": "annual", "publisher": "USDA NASS", "tags": "['agriculture', 'crops', 'yield']"
| # | dataset_id | title | organization_name | notes_description | metadata_created | metadata_modified |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Resources & Files objects from data.gov. All fields typed and schema-versioned.
"resource_id": "res-9876-abcd", "dataset_id": "gov-usda-12345", "name": "2025 Crop Yield Data", "format": "CSV", "download_url": "https://www.nass.usda.gov/data/yield2025.csv", "size_bytes": 14589200, "last_modified": "2026-01-15T14:30:00Z"
| # | resource_id | dataset_id | name | format | download_url | size_bytes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Organizations objects from data.gov. All fields typed and schema-versioned.
"org_id": "org-usda", "name": "usda-gov", "title": "Department of Agriculture", "dataset_count": 4821, "state": "active", "approval_status": "approved", "created": "2013-05-18T12:00:00Z"
| # | org_id | name | title | description | dataset_count | image_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Tags & Categories objects from data.gov. All fields typed and schema-versioned.
"tag_id": "tag-climate", "name": "climate-change", "display_name": "Climate Change", "dataset_count": 12450, "state": "active", "related_tags": "['weather', 'environment', 'emissions']"
| # | tag_id | name | display_name | vocabulary_id | dataset_count | state |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Geospatial Data objects from data.gov. All fields typed and schema-versioned.
"dataset_id": "gov-noaa-555", "spatial_text": "United States", "bounding_box": "[-124.7844079, 24.7433195, -66.9513812, 49.3457868]", "coordinates_type": "Polygon", "region": "North America", "projection": "EPSG:4326"
| # | dataset_id | spatial_text | bounding_box | coordinates_type | region | mapping_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Data.gov scraper navigates the CKAN architecture, normalising inconsistent agency metadata, capturing resource download links, and tracking update cadences across 350,000 federal datasets.
Extract data via the native CKAN API, with automated fallback to HTML DOM parsing for undocumented or broken endpoints.
Capture and validate download URLs for CSV, JSON, XML, and PDF resources. We flag dead links before they break your downstream pipelines.
Map datasets to their parent organizations and sub-agencies. Track dataset volume and publication frequency per department.
Monitor metadata_modified timestamps to detect new data releases. Trigger webhooks when high-value datasets are updated.
Parse spatial fields, bounding boxes, and GeoJSON coordinates for GIS and mapping workflows.
Filter extractions by open-source licenses, public domain markers, and specific file formats to ensure compliance and usability.
Standardise inconsistent field names and date formats across different federal and state publishers into a single unified schema.
Maintain a changelog of dataset descriptions and tags over time to monitor shifting government data priorities.
Run weekly catalogue syncs or configure hourly diffing for critical fast-moving datasets like weather or financial indicators.
Brief in. Clean data out.
Provide target agencies, tags, or search queries. We design the extraction schema together.
We configure Scrapy crawlers, CKAN API handlers, and proxy rotation for data.gov.
Schema validation, broken link detection, and metadata normalisation checks before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Federal data portals are notorious for inconsistent schemas, broken links, and unpredictable update cycles. Here is how we maintain pipeline stability.
Different federal agencies use different conventions for dates, tags, and publisher fields. We apply normalisation rules at the extraction layer so your warehouse receives a clean, predictable schema regardless of the source agency.
Government data portals suffer from link rot. Our pipeline tests resource URLs via HTTP HEAD requests during the crawl, flagging 404s and redirects so you do not ingest dead links.
Default CKAN API limits cap pagination at 100,000 records. We use search partitioning by date ranges and organization IDs to extract the entire 350,000+ dataset catalogue without hitting offset errors.
We maintain a hash index of last-seen metadata. Subsequent runs only push diffs for datasets where the metadata_modified timestamp has changed, reducing compute cost and downstream processing load.
While data.gov is public, aggressive scraping triggers rate limits and IP blocks. We distribute requests across US residential proxies with randomised timing to maintain high throughput without disruption.
Research institutions ingest NOAA and NASA datasets to train predictive climate models and track historical weather patterns.
Quant funds monitor Treasury, Census, and Bureau of Labor Statistics releases for macroeconomic indicators and demographic shifts.
Healthcare analytics firms track CDC and FDA data releases to model disease spread, drug approvals, and public health outcomes.
PropTech companies extract FEMA flood zones, HUD housing data, and local zoning shapefiles to inform property valuation models.
Machine learning teams use the vast corpus of government reports, statistics, and legal documents to train domain-specific LLMs.
Defense and civilian contractors track agency spending data, contract awards, and budget allocations to identify procurement trends.
"Data.gov contains the most valuable public datasets in the world, but navigating 300,000 inconsistent agency schemas requires dedicated infrastructure."
Most data teams waste weeks writing custom parsers for individual federal agencies. DataFlirt centralises this extraction, normalising CKAN metadata, validating resource URLs, and delivering clean, queryable records. Your engineers focus on analysis, not broken government APIs.
Everything supported by our data.gov scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles API orchestration and pagination logic. Playwright handles custom agency portals that rely heavily on client-side rendering.
We use US-based residential proxies to distribute request volume, preventing rate limits and IP bans from federal firewalls.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About data.gov scraping, legality, and pipeline operations.
Ask us directly →Yes. Data.gov aggregates public domain datasets published by the US federal government. This data is explicitly intended for public access and reuse. We strictly target public metadata and resources, avoiding any authenticated or classified systems.
We primarily extract the dataset metadata and validate the resource download URLs. However, we can configure pipelines to automatically download specific file types (like CSVs or JSONs) directly to your S3 bucket upon detection.
Link rot is common on data.gov. Our pipeline performs HTTP HEAD requests on resource URLs during extraction. We include a status code field in the delivery schema so you can filter out 404s before they hit your warehouse.
Yes. We monitor the metadata_modified field and maintain a hash of the record. When an agency pushes new data, our change-detection diffing captures the update and delivers the new record on the next pipeline run.
Yes. Because data.gov centralises metadata via the CKAN architecture, our pipeline captures records from all participating federal, state, and local agencies listed in the portal.
Absolutely. We provide a sample run of up to 1,000 dataset records for your specified agencies or tags during the scoping process, allowing you to validate the schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily feed of climate datasets or a full catalogue dump of federal financial records, we build and operate the pipeline. Tell us what you need.
