Data Pipeline

Built for Accuracy at Enterprise Scale

Our five-stage pipeline transforms fragmented US property records into validated, structured datasets — continuously, reliably, and at a scale that enterprise teams can depend on.

99.9%Delivery Uptime
1M+Weekly Records
24hrMax Refresh Lag
99.7%Accuracy Rate
Pipeline Status All Systems Live
01
Source Discovery
Multiple active sources monitored
Continuous
02
Data Ingestion
Raw collection & deduplication
Live
03
Normalization
Schema standardization & enrichment
Automated
04
Quality Validation
28-point automated checks
Automated
05
Delivery
API · Snowflake · Flat Files
Live
Pipeline Stages

Five Stages, Zero Compromises

Each stage is independently monitored, with automated alerting and fallback mechanisms to guarantee consistency at every step.

01
Source Discovery Finding and qualifying every data source
+

The foundation of data quality is source quality. Our discovery engine continuously monitors over 2,400 US property data sources — county assessors, MLS feeds, public records, rental listing platforms, and proprietary scraping targets — and evaluates each for freshness, completeness, and reliability.

  • Source coverage spans all 50 states and 3,200+ counties
  • Each source is scored on freshness, coverage density, and historical reliability
  • New sources are automatically detected and queued for onboarding evaluation
  • Degraded sources trigger automatic fallback routing to secondary feeds
Source RegistryHealth MonitoringAutomated DiscoveryFallback Routing
2,400+
Active sources monitored across all 50 states
3,200+
US counties with active data collection
15min
Average source health check interval
02
Data Ingestion Collecting and deduplicating at scale
+

Raw property data arrives in dozens of formats — XML feeds, JSON APIs, HTML pages, CSV exports, PDF documents. Our ingestion layer handles all of them, normalizing the extraction layer before any data enters the pipeline. Cross-source deduplication runs in real time using address-level entity resolution.

  • Multi-format ingestion: XML, JSON, CSV, HTML, structured PDFs
  • Real-time deduplication using address entity resolution algorithms
  • Incremental ingestion — only changed records are re-processed
  • Full raw data archive maintained for audit and reprocessing
Entity ResolutionMulti-format ParsingIncremental SyncRaw Archive
1M+
Records ingested per week across all property types
<0.1%
Duplicate record rate after entity resolution
100%
Raw data archived for reprocessing and audits
03
Normalization & Enrichment Standardizing every record to a consistent schema
+

This is where data becomes intelligence. Normalization applies standardized field definitions across all sources — resolving the thousands of variations in how US property data is described. Enrichment adds derived attributes: rental yield estimates, neighborhood demand scores, price trend indices, and market velocity signals.

  • Unified schema with 80+ standardized attributes across all property types
  • Address standardization to USPS format with geocoding to lat/lon
  • Derived attributes: rental yield, demand index, price trend, market velocity
  • Conflict resolution when multiple sources disagree on the same field
Schema UnificationGeocodingDerived SignalsConflict Resolution
80+
Standardized attributes in the unified property schema
12
Derived intelligence signals added per property record
99.8%
Address geocoding success rate across all 50 states
04
Quality Validation 28-point automated checks on every record
+

No record reaches our delivery layer without passing a battery of automated quality checks. We validate completeness, range plausibility, cross-field consistency, and historical continuity. Records that fail checks are quarantined for review or flagged with confidence scores rather than silently passed through.

  • Completeness checks: required fields, null rates, data freshness
  • Range validation: price, size, year built within statistically valid bounds
  • Cross-field consistency: bedrooms vs. square footage ratios, price per sq ft
  • Historical continuity: flag anomalous changes vs. prior period
28-Point ChecksConfidence ScoringAnomaly DetectionQuarantine Queue
28
Automated validation checks applied to every record
99.7%
Records passing all validation checks before delivery
<4hr
Mean time to resolve quarantined record batches
05
Delivery & Integration Getting data where your systems need it
+

The final stage is delivery — and we've built three enterprise-grade delivery mechanisms to fit any technical stack. REST API for real-time product integrations, native Snowflake data sharing for analytics warehouses, and scheduled flat file delivery for batch processing workflows. All with SLA guarantees and 24/7 monitoring.

  • REST API: sub-100ms responses, up to 10K req/min, full OpenAPI 3.0 spec
  • Snowflake: zero-copy native sharing, real-time propagation, cross-region
  • Flat files: CSV or Parquet, delivered to S3 / SFTP / Azure Blob on schedule
  • 99.9% delivery SLA with automated failover and incident notification
REST APISnowflakeCSV / ParquetS3 / SFTPWebhooks
99.9%
Delivery uptime SLA across all delivery methods
<80ms
Average REST API response time under normal load
3
Delivery methods: API, Snowflake, and scheduled flat files
Data Quality

Quality Is Not an Afterthought

Every record that leaves our pipeline has passed four categories of quality checks. Here's how we maintain 99.7% accuracy at scale.

Completeness Validation
Required fields are checked against minimum population thresholds. Records with critical missing data are quarantined, not passed through.
Range Plausibility
Prices, sizes, and other numerical fields are checked against statistical bounds derived from historical data for each geography and property type.
Cross-Field Consistency
Related fields are checked for logical consistency — bedroom-to-square-footage ratios, price-per-sq-ft outliers, address-to-geocode matching.
Historical Continuity
Unusual changes vs. the prior period — sudden price jumps, field value reversals — trigger anomaly flags and manual review before delivery.
Accuracy by Data Dimension
99%
Address
95%
Pricing
97%
Property
99.7%
Overall Record Accuracy Rate
Schema validated
Geocoded & verified
Deduplicated
Enriched & scored
Delivery Methods

Three Ways to Receive Your Data

Choose the delivery method that fits your technical stack — or combine them for different use cases within the same contract.

Method 01
REST API
Real-time programmatic access with sub-100ms response times. Ideal for applications that need live property data at query time.
OpenAPI 3.0 full documentation
Python & Node.js SDKs included
Up to 10,000 requests per minute
Sandbox environment available
Webhook support for push updates
Method 02
Snowflake Data Share
Zero-copy native Snowflake sharing. Query our datasets directly in your Snowflake account — no pipelines, no file transfers, no ingestion overhead.
Zero-copy — no ETL required
Real-time propagation of updates
Cross-region sharing supported
Works with existing Snowflake setup
Full historical access from day one
Method 03
Flat File Delivery
Scheduled delivery of CSV or columnar Parquet files to your S3 bucket, SFTP server, or Azure Blob Storage — on your preferred cadence.
CSV and Parquet formats available
Daily, weekly, or monthly schedules
Full or incremental delivery options
S3, SFTP, Azure Blob supported
MD5 checksums on every delivery
Service Level Agreement

Guaranteed Performance

99.9%Delivery Uptime
<24hrMax Data Refresh Lag
<4hrIncident Response Time
99.7%Record Accuracy Rate

See the Pipeline In Action

Request a sample dataset and review our data quality documentation firsthand — no lengthy procurement process required.

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