We extract CPI, PPI, JOLTS, and regional wage data from bls.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 Consumer Price Index objects from bls.gov. All fields typed and schema-versioned.
"series_id": "CUUR0000SA0", "year": 2024, "period": "M01", "value": 309.685, "area_code": "0000", "item_code": "SA0", "seasonally_adjusted": false
| # | series_id | area_code | item_code | year | period | value |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Producer Price Index objects from bls.gov. All fields typed and schema-versioned.
"series_id": "PCU311---311---", "industry_code": "311", "product_code": "311---", "year": 2024, "period": "M01", "value": 254.3, "base_date": "198412"
| # | series_id | industry_code | product_code | year | period | value |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Employment Stats objects from bls.gov. All fields typed and schema-versioned.
"series_id": "LASST060000000000003", "year": 2024, "period": "M01", "labor_force": 19345000, "unemployment": 1005940, "unemployment_rate": 5.2, "preliminary": true
| # | series_id | state_code | area_code | year | period | labor_force |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for JOLTS Data objects from bls.gov. All fields typed and schema-versioned.
"series_id": "JTU00000000JOL", "industry_code": "000000", "data_element_code": "JO", "year": 2024, "period": "M01", "rate": 5.3, "level": 8756
| # | series_id | industry_code | region_code | data_element_code | year | period |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Occupational Wages objects from bls.gov. All fields typed and schema-versioned.
"occ_code": "15-1252", "occ_title": "Software Developers", "total_employment": 1476900, "hourly_mean_wage": 63.92, "annual_mean_wage": 132930, "release_year": 2023
| # | area_type | area_code | occ_code | occ_title | total_employment | hourly_mean_wage |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our bls.gov scraper handles legacy HTML structures, FTP flat files, and undocumented schema changes to deliver continuous, analysis-ready economic data.
Extract continuous historical data across CPI, PPI, and employment metrics without manual file joining.
Navigate archaic government DOM structures and complex nested tables to extract precise data points.
Capture preliminary data and automatically update records when the BLS publishes revised figures.
Map complex BLS geographic codes to standard state, MSA, and county identifiers for easy joining.
Translate BLS-specific industry classifications into standard NAICS codes across all datasets.
Parse massive text and CSV files from the BLS FTP servers into structured warehouse tables.
Detect and adapt to unannounced changes in BLS reporting formats and table structures.
Combine wage data with local CPI figures to calculate real wage growth automatically.
Run pipelines immediately following BLS release schedules to ensure minimal data latency.
Brief in. Clean data out.
Provide the specific series IDs, indexes, or regional data you need. We design the extraction schema.
We configure Scrapy crawlers and flat-file parsers to handle bls.gov rate limits and structures.
Schema validation, continuity checks, and historical data reconciliation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Government websites present unique scraping hurdles. Here is how we build resilient pipelines for bls.gov data.
Many BLS pages rely on nested tables and outdated markup. We build specific XPath and CSS fallback chains to extract data reliably despite structural inconsistencies.
The BLS enforces strict IP-based rate limiting. We manage request concurrency and rotate datacenter proxies to maintain high throughput without triggering blocks.
Economic data is frequently revised months after initial publication. Our pipelines track preliminary flags and automatically update historical warehouse records when revisions occur.
For bulk historical data, the BLS provides massive, poorly formatted text files. We parse, type-cast, and normalise these files into clean Parquet columns.
Government sites often change formats without notice. We monitor null rates and schema drift, alerting our engineers to adapt parsers before downstream pipelines fail.
Quantitative funds feed CPI, PPI, and employment time series into models to predict interest rate movements.
HR platforms use OEWS data to set competitive compensation bands across different MSAs and occupations.
Supply chain teams use granular PPI data to negotiate material contracts with automatic inflation escalators.
Commercial real estate firms track local employment growth and wage trends to identify emerging markets.
Universities require clean, continuous historical datasets for long term economic and sociological studies.
Retailers combine local unemployment rates and wage data to determine optimal locations for new stores.
"Macroeconomic forecasting relies on bls.gov data, but extracting clean time series from decades of legacy HTML tables is an infrastructure nightmare."
Most teams underestimate the investment required: parsing archaic government markup, handling unannounced format changes, and normalising historical revisions requires constant maintenance. DataFlirt absorbs that complexity so your quants can focus on the models, not the ingestion layer.
Everything supported by our bls.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 crawl orchestration, rate limiting, and HTML parsing. Custom middlewares manage retry logic and connection pooling for government servers.
We maintain pools of datacenter proxies to distribute requests evenly, preventing IP bans while respecting BLS concurrency limits.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling to align with BLS data release calendars. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About bls.gov scraping, legality, and pipeline operations.
Ask us directly →Yes. BLS data is public domain information provided by the US government. Scraping is permissible provided it adheres to their terms of service regarding rate limits and server load. DataFlirt manages request concurrency to ensure full compliance.
The BLS Public Data API has strict daily query limits, limits on years per request, and often lacks the most granular regional or cross-tabulated data found in their flat files and HTML tables. Our pipelines bypass these API limitations by parsing the raw data sources.
Economic indicators are frequently revised. We track the 'preliminary' flags on data points. Subsequent pipeline runs check for updates to historical periods and overwrite the warehouse records with the finalised figures.
Yes. We can extract time series data going back decades, depending on the specific index. We normalise historical file formats to match current schemas, providing a continuous dataset.
We schedule pipelines to run immediately following the official BLS release times (typically 8:30 AM EST). Data is usually delivered to your warehouse within minutes of publication.
Yes. Where the BLS uses legacy or proprietary industry codes, we can map these to standard North American Industry Classification System (NAICS) codes for easier joining with your internal data.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop fighting legacy HTML tables and manual file downloads. Tell us which BLS series you need, and we will deliver clean time series to your warehouse.
