Transforming Scout's Data Platform for Scale and Speed

Scout is a real estate intelligence platform that helps connect buyers, sellers, and real estate professionals using large-scale consumer and property datasets. The platform relies on matching consumer records, property transactions, and agent information to generate actionable insights for real estate marketing and prospecting.

As Scout's data footprint expanded beyond 200 million consumer records and millions of transaction records, the existing PostgreSQL-based architecture struggled to keep pace with performance and cost requirements.

As Scout's database grew, PostgreSQL became increasingly expensive and inefficient for analytical workloads, fuzzy matching, and large-scale search operations.

Key Issues

• More than 200 million consumer records required frequent processing and matching.
• Long-running analytical jobs took up to 84 hours to complete.
• PostgreSQL infrastructure costs reached approximately $4,000 per month.
• Fuzzy name matching and address matching required significant compute resources.
• Search and reporting workloads were competing with operational workloads.
• The platform needed a scalable architecture capable of supporting future growth without exponentially increasing costs.

Without modernization, performance bottlenecks and infrastructure expenses would continue to increase as data volumes grew.

To address scalability and performance limitations, a modern cloud-native architecture was designed and implemented.

Data Platform Modernization

The project involved migrating the existing PostgreSQL-centric architecture to a purpose-built ecosystem consisting of:

• Snowflake for large-scale data warehousing and analytical processing.
• OpenSearch for high-performance search and fuzzy matching.
• Amazon S3 for low-cost staging and storage.
• Python automation pipelines for data ingestion, transformation, and orchestration.

Key Deliverables

• Migrated 200M+ consumer records into Snowflake.
• Built automated Snowflake-to-S3-to-OpenSearch pipelines.
• Designed scalable denormalization and enrichment workflows.
• Implemented consumer matching and address matching services.
• Optimized search workloads through OpenSearch indexing.
• Eliminated PostgreSQL as the primary query engine.
• Created a cost-efficient architecture optimized for both analytics and search.

To support more than 200 million records, the platform was rebuilt using Snowflake, OpenSearch, AWS, and Python. Each technology was chosen for a specific purpose, resulting in faster processing, lower infrastructure costs, and a more scalable foundation for future growth.

99% Cost Reduction

Infrastructure costs decreased from approximately $4,000/month to $40/month by replacing an oversized PostgreSQL environment with Snowflake and OpenSearch.

Faster Processing

• Reduced major processing workloads from roughly 84 hours to 2 hours.
• Significantly improved search and matching performance across hundreds of millions of records.

Large-Scale Data Optimization

• Successfully processed and optimized 200M+ consumer records.
• Supported 15M+ real estate transaction records.
• Enabled search across millions of agent and property records.

Improved Architecture

• Separated analytical processing from search workloads.
• Reduced operational overhead.
• Increased reliability and scalability for future growth.