Stop struggling with FAA registration data. Get clean, queryable aircraft registry tables in seconds.
Hangarbay downloads, normalizes and indexes the complete FAA aircraft registry so you can skip straight to analysis. No more hunting for files to download, or joining fragmented tables, or decoding cryptic lookup relationships. One command gets you 307,000+ aircraft registrations, owners and specifications ready to query with SQL or pandas.
This is a tool built for researchers, data journalists and aviation analysts who need fast, reliable and repeatable workflows.
Requirements: Python 3.9+
# Install
pip install hangarbay
# One-time setup
import hangarbay as hb
hb.load_data() # Downloads & processes FAA data to ~/.hangarbay/data/
# Look up aircraft
df = hb.search("N221LA")
# Find fleets
fleet = hb.fleet("United Airlines")
fleet = hb.fleet("LAPD|Los Angeles Police", state="CA")
# Custom SQL
df = hb.query("""
SELECT maker, COUNT(*) as count
FROM aircraft_decoded
WHERE year_mfr > 2020
GROUP BY maker
ORDER BY count DESC
""")
# Check data age
info = hb.status()
print(f"Data is {info['age_days']} days old")# Install
pip install hangarbay
# Run the full pipeline (< 1 minute, depending on your connection)
hangar update # Download, normalize and publish (in one step)
# Or run steps individually
hangar fetch # Download FAA data
hangar normalize # Parse to typed Parquet tables
hangar publish # Build DuckDB + SQLite FTS indexes
# Check data status and age
hangar status
# Start querying!
hangar search N757AF
hangar sql "SELECT COUNT(*) FROM aircraft"Hangarbay downloads FAA aircraft registration data, normalizes it into typed tables and provides fast SQL querying:
- 307K+ aircraft registrations with owner and address information
- 93K+ make/model references (Cessna, Piper, Boeing, etc.)
- 4,700+ engine specifications (horsepower, type, manufacturer)
- DuckDB for analytical SQL queries (sub-second on 300K+ rows)
- SQLite FTS5 for full-text search by owner name or address
- Parquet files for efficient columnar storage
- Data lineage tracking with SHA256 checksums and version metadata
- Shared data directory at
~/.hangarbay/data/- works from any project or notebook
Look up any aircraft registration with decoded status codes and human-readable output:
hangar search N221LAAircraft Registration: N221LA
Owner: LAPD AIR SUPPORT DIVISION
Address: 555 RAMIREZ ST SPC 475
Location: LOS ANGELES, CA 90012
Make & Model: AIRBUS HELICOPTERS INC AS350B3
Year Manufactured: 2014
Serial Number: 7900
Registration Status: Valid
Certificate Type: Standard Airworthiness - Normal
Certificate Issue: Jun 15, 2015
Status Date: May 19, 2023
Expiration: Jun 30, 2028
Mode S Code: A1E5B3
Find all aircraft owned by a person or company:
# Search by owner name (case-insensitive)
hangar fleet "United Airlines"
hangar fleet "LAPD"
hangar fleet "NetJets"
# OR logic with pipe separator (great for finding multiple airlines)
hangar fleet "LAPD|Los Angeles Police" # Either pattern matches
hangar fleet "Delta|American|United" # Any of the three
# Filter by state
hangar fleet "Delta" --state GA
# Export to CSV
hangar fleet "Boeing" --export boeing_fleet.csv
# Limit results
hangar fleet "Cessna" --limit 20Execute analytical queries with pretty output:
# Total aircraft
hangar sql "SELECT COUNT(*) FROM aircraft"
# Use decoded views for readable output
hangar sql "SELECT * FROM aircraft_decoded WHERE year_mfr > 2020 LIMIT 10"
hangar sql "SELECT * FROM owners_clean WHERE owner_name LIKE '%boeing%'" -i
# Top manufacturers
hangar sql "SELECT maker, COUNT(*) as count FROM aircraft
JOIN aircraft_make_model USING(mfr_mdl_code)
WHERE maker != '' GROUP BY 1 ORDER BY 2 DESC LIMIT 10"
# Lookup status codes
hangar sql "SELECT * FROM status_codes"
# Top states by registrations
hangar sql "SELECT state_std, COUNT(*) as count FROM owners
WHERE state_std != '' GROUP BY 1 ORDER BY 2 DESC LIMIT 10"
# Find all aircraft of a specific model (e.g., MD-11) across all owners
hangar sql "SELECT o.owner_name, COUNT(*) as count
FROM aircraft_decoded a
JOIN owners_clean o ON a.n_number = o.n_number
WHERE a.model LIKE '%MD-11%'
GROUP BY o.owner_name
ORDER BY count DESC"Output Formats:
# Pretty table (default)
hangar sql "SELECT * FROM status_codes LIMIT 3"
# JSON output (for APIs/scripts)
hangar sql "SELECT * FROM status_codes LIMIT 3" --output-format json
# CSV output (for Excel/spreadsheets)
hangar sql "SELECT * FROM status_codes LIMIT 3" --output-format csvCore Tables:
- aircraft - Current registration facts (N-number, serial, make/model, year, status)
- registrations - Canonical registration state (type, status, dates)
- owners - Owner records with raw and standardized addresses
- owners_summary - Aggregated ownership (co-owners, counts)
- aircraft_make_model - Make/model reference (Cessna 172, Boeing 737, etc.)
- engines - Engine specifications (manufacturer, horsepower, type)
Decoded Views (Recommended):
- aircraft_decoded - Aircraft with decoded status codes and joined make/model
- owners_clean - Simplified owner table (standardized fields only, decoded owner type)
Reference Tables:
- status_codes - Registration status code lookups
- airworthiness_classes - Airworthiness certificate class lookups
- owner_types - Owner type code lookups
- Type safety: PyArrow schemas enforce correct data types (dates, integers, strings)
- Address standardization: Uppercase, state/ZIP normalization, whitespace cleanup
- Deterministic IDs: xxhash64 ensures consistent owner IDs across pipeline runs
- File verification: SHA256 checksums validate all downloads
- Version tracking: Manifest records schema versions and row counts for each snapshot
FAA Data (MASTER, ACFTREF, ENGINE)
↓ fetch (with SHA256 verification)
Raw Text Files (275 MB)
↓ normalize (PyArrow type casting)
Typed Parquet Tables (36 MB)
↓ publish (indexing)
DuckDB (106 MB) + SQLite FTS (55 MB)
↓ query
CLI / Python API
To contribute or modify the code:
# Clone and install for development
git clone https://github.com/stiles/hangarbay.git
cd hangarbay
pip install -e ".[dev]"
# Run tests
make test
# Update data
make update # or: hangar update
# Check data status
make status # or: hangar status
# Full pipeline (individual steps)
make all # fetch, normalize, publish, verify
# Individual steps
make fetch
make normalize
make publish
# Clean intermediate files
make cleanMIT