We extract ACS, Decennial Census, economic indicators, and geographic boundary data. 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 Demographics (ACS) objects from census.gov. All fields typed and schema-versioned.
"fips_code": "06037207102", "state": "California", "county": "Los Angeles County", "tract": "2071.02", "total_population": 4821, "median_income": 82450.0, "median_age": 36.4, "poverty_rate": 12.1, "education_bachelors_pct": 34.2
| # | fips_code | state | county | tract | total_population | median_income |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Economic Indicators objects from census.gov. All fields typed and schema-versioned.
"survey_id": "CBP2021", "naics_code": "511210", "industry_name": "Software Publishers", "total_establishments": 14205, "annual_payroll": 124500000, "total_employees": 842100, "year": 2021
| # | survey_id | naics_code | industry_name | total_establishments | annual_payroll | total_employees |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Housing objects from census.gov. All fields typed and schema-versioned.
"fips_code": "48201", "housing_units": 1842910, "occupied_units": 1698200, "vacant_units": 144710, "median_home_value": 245000.0, "median_rent": 1250.0, "homeowner_vacancy_rate": 1.4, "rental_vacancy_rate": 6.8
| # | fips_code | housing_units | occupied_units | vacant_units | median_home_value | median_rent |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Geographic Boundaries objects from census.gov. All fields typed and schema-versioned.
"geoid": "36061", "name": "New York County", "lsad": "06", "aland": 58684203, "awater": 28551405, "intptlat": "+40.7766428", "intptlon": "-073.9701871", "geometry_geojson": "{"type": "Polygon", "coordinates": [...]}"
| # | geoid | name | lsad | aland | awater | intptlat |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Business Patterns objects from census.gov. All fields typed and schema-versioned.
"fips_code": "17031", "naics_code": "722511", "est_total": 4102, "est_1_4_emp": 841, "est_5_9_emp": 1024, "est_10_19_emp": 1150, "annual_payroll_1000s": 2410500, "noise_flag": "G"
| # | fips_code | naics_code | est_total | est_1_4_emp | est_5_9_emp | est_10_19_emp |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Census pipeline handles every layer of the platform: API orchestration, FTP bulk downloads, variable mapping, and geographic traversal — delivering clean data directly to your warehouse.
Extract American Community Survey 1-year and 5-year estimates across all geographic levels.
Pull detailed industry statistics, payroll, and establishment counts by NAICS code.
Traverse data from national level down to state, county, tract, and block group FIPS codes.
Handle Census API token rotation, rate limiting, and backoff protocols automatically.
Download, decompress, and parse large FTP text and CSV dumps into normalised warehouse tables.
Extract and transform shapefiles into queryable GeoJSON or WKT formats for spatial analysis.
Process Public Use Microdata Sample files for custom cross-tabulations and statistical modelling.
Normalise variables across different census surveys using standard FIPS and NAICS identifiers.
Run continuous pipelines aligned with Census Bureau release schedules to maintain fresh data.
Brief in. Clean data out.
Provide target surveys, geographic granularity, and variable lists. We design the extraction schema together.
We configure API clients, FTP parsers, variable mapping, and pagination logic for census.gov.
Schema validation, null-rate checks, FIPS code verification, and sample data review before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Working with government data systems requires navigating archaic protocols and complex metadata. Here is how we build resilience.
The Census API limits response sizes and requires complex nested queries to extract full state or national datasets. We handle the loop logic across all FIPS codes.
Census variable names require external metadata dictionaries to translate into human-readable columns. We map these automatically during extraction.
Historical data often resides in massive, poorly formatted text files on Census FTP servers. We automate the download, parsing, and cleaning.
Matching tracts to counties to states requires strict adherence to FIPS standards. We enforce geographic relational integrity across all datasets.
Even with API keys, Census servers throttle aggressive extraction. We manage concurrency and backoff to ensure reliable delivery without connection resets.
Retailers and developers use tract-level income and population data to evaluate new store locations.
Analysts track county business patterns and retail trade surveys to build macroeconomic models.
Marketing agencies use demographic and housing statistics to define target audience profiles by zip code.
Researchers process PUMS microdata for sociological studies and public policy impact assessments.
Providers analyse age distribution and insurance coverage statistics to plan facility expansions.
Insurers correlate housing age and geographic data with claims models to optimise underwriting.
"The Census Bureau holds the most comprehensive demographic dataset in the US, but extracting it across thousands of FIPS codes requires serious engineering."
Working with census.gov data means navigating archaic FTP servers, complex variable dictionaries, and strict API limits. DataFlirt manages the orchestration, variable mapping, and geographic joins so your analysts can query clean demographic tables directly in your warehouse.
Everything supported by our census.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.
Airflow manages complex DAGs to query the Census API, download bulk FTP files, and merge datasets.
Python-based transformation pipelines map arcane Census variable codes to human-readable schema definitions.
Pipelines run on ECS and Lambda, pushing columnar Parquet files directly to your cloud data warehouse.
Data delivered to where your team already works — no new tooling required.
About census.gov scraping, legality, and pipeline operations.
Ask us directly →Yes. US Census data is public domain and explicitly published for public use. We adhere to their API terms of service and usage guidelines.
We primarily use the official Census API and bulk FTP servers for structured data extraction. We only scrape web interfaces for metadata or unindexed reports.
We integrate the Census Bureau's metadata dictionaries into our pipeline, automatically renaming variables like 'B19013_001E' to 'median_household_income'.
Yes. We can traverse geographic hierarchies down to the block group level using standard FIPS codes.
We schedule pipelines based on the Census Bureau's release calendar, ensuring you receive new ACS or Decennial data as soon as it is published.
Yes. We can extract TIGER/Line shapefiles and convert them into standard spatial formats like GeoJSON or WKT for your GIS systems.
Our smallest packages start at defined state-level extractions. For national, multi-survey pipelines, we price based on compute and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off demographic dump or a continuous pipeline for economic indicators — we scope, build, and operate the pipeline. Tell us what you need.
