Real Estate Market Intelligent Pipeline

Overview

Built a production-ready ELT platform for property market data. The system combines batch and streaming ingestion, automated quality checks, and cost-efficient warehouse design in BigQuery to power analytics, dashboards, and downstream applications.

Challenge

Real estate data arrives from many sources at different cadences and formats (APIs, files, scrapers). The pipeline needed to support frequent updates, automatic validation, and a warehouse model optimized for fast and inexpensive queries at scale.

Solution

Designed a unified ELT architecture:

• Ingestion: Scheduled batch loads for large historical pulls and a lightweight streaming path for incremental updates.
• Quality: Automated validation on ingest (schema, nulls, ranges) with failure alerts and quarantining of bad records.
• Transform: Modeled raw data into clean, analytics-friendly tables with partitioning and clustering to reduce cost and improve query speed.
• Orchestration & Monitoring: End-to-end jobs with observability on freshness, volume, and failures.

Technical Implementation

Core components and tools:

• Python services for ingestion (batch + streaming) and transformation logic
• Google BigQuery as the warehouse with partitioned + clustered tables
• dbt for modeling and documentation of the semantic layer
• Apache Airflow for orchestration and SLA/freshness monitoring
• Cloud Run / Cloud Functions for stateless ingestion jobs
• Pub/Sub pipeline for streaming updates from scrapers and webhooks