Data Pipeline for Real Estate Firm
One of the largest real estate investment services firm of the United States, struggled with manual integration of data
Data Engineering & Cloud-Native Integrat
AWS, Dagster, DuckDB, PostgreSQL, S3
Client challenge
One of the largest real estate investment services firm of the United States, struggled with manual integration of data from third-party sources (Reonomy, Crexi, and Alphamap) into their CRM. This created multiple critical issues:
• Agents wasted valuable time on data management instead of sales activities.
• Manual processes introduced errors, duplicates, and inconsistencies into the CRM.
• Infrequent and slow data updates hindered market responsiveness.
• The existing workflow couldn't efficiently scale with increasing data volumes.
• Complex integration with their specific database schema was error-prone.
• Lack of automated tracking made data lineage and auditing impossible.
Solution delivered
Renaiss engineered "Hyperion," a cloud-native data pipeline on AWS implementing data engineering best practices:
• Data-Aware Orchestration: Implemented Dagster to manage end-to-end workflow, dependencies, scheduling, and automatic metadata capture for governance.
• Efficient Data Processing: Utilized DuckDB for high-performance transformation and quality enforcement (deduplication, standardization).
• Scalable Architecture: Deployed containerized applications on AWS EKS for resilience and scalability.
• Decoupled Storage: Leveraged AWS S3 with distinct data stages (raw/transformed) for durability and auditability.
• Resilient Loading: Created SQS-driven asynchronous mechanism for efficient bulk loading into Gemini PostgreSQL.
Business Results
The Hyperion pipeline delivered transformative outcomes for the client:
• Eliminated Manual Effort: Automated end-to-end data handling freed significant operational capacity.
• Enhanced Agent Productivity: Agents could focus entirely on revenue-generating activities with direct access to accurate, timely data.
• Improved Data Quality: Standardized rules enforced consistency and accuracy across all sources.
• Strengthened Decision Making: Clean, trustworthy data facilitated more accurate reporting and insights.
• Established Governance: Clear data lineage and processing history met auditability requirements.
• Future-Proofed Operations: The scalable AWS architecture can easily handle increased data loads and additional sources.
• Increased Resilience: Decoupled architecture enhanced system robustness against downstream issues