Case Study
One of the largest real estate investment services firms in the United States was losing time and accuracy to a problem that's easy to underestimate: manual data integration. Their agents were pulling information from three external sources — Reonomy, Crexi, and Alphamap — and manually pushing it into their CRM. The result was a system full of duplicates, inconsistencies, and outdated records. Agents spent hours on data management instead of closing deals. And as data volumes grew, the process didn't just slow down — it broke.
Renaiss engineered Hyperion — a cloud-native data pipeline built on AWS that automated the entire data lifecycle from ingestion to CRM loading. At its core, Dagster handles orchestration: managing dependencies, scheduling, and capturing metadata automatically for governance and auditing. DuckDB powers the transformation layer, enforcing deduplication and standardization across all incoming sources.
The architecture runs on AWS EKS for resilience and scalability, with S3 as a decoupled storage layer that separates raw and transformed data for full auditability. An SQS-driven asynchronous loading mechanism handles bulk writes into Gemini PostgreSQL without bottlenecks. Every piece was built to be independent, observable, and ready to absorb more data sources as the business grows.
Hyperion eliminated the manual effort entirely. Agents stopped managing data and went back to selling. Data quality improved across every source — standardization rules enforced consistency that the manual process never could. Reporting became reliable, and with clear data lineage in place, the firm met its auditability requirements for the first time. The architecture is now positioned to scale with new data volumes and additional third-party integrations without a rebuild.
End-to-end automation replaced a process that was consuming agent time and introducing errors at every step.
With accurate, timely data available directly in the CRM, the team focused entirely on revenue-generating activity.
Deduplication and standardization rules applied consistently across Reonomy, Crexi, and Alphamap — no exceptions.
Every record has a traceable processing history, meeting governance requirements the previous workflow made impossible.
Every record has a traceable processing history, meeting governance requirements the previous workflow made impossible.
The AWS-native infrastructure handles growing data volumes and new integrations without structural changes.
Discovery & Data Mapping
01 / 05
Orchestration Design with Dagster
02 / 05
Transformation & Quality Enforcement
03/ 05
Scalable Infrastructure on AWS
04 / 05
Governance & Handoff
05 / 05
We audited the client's existing CRM schema, third-party data sources, and manual workflows to understand where errors were introduced and what the pipeline needed to enforce.
We implemented Dagster as the orchestration layer — managing scheduling, dependencies, and automatic metadata capture so every run is observable and auditable from end to end.
We used DuckDB to build a high-performance transformation layer that standardizes, deduplicates, and validates data before it moves downstream — regardless of source format.
We deployed containerized applications on EKS, configured S3 with distinct raw and transformed stages, and built an SQS-driven loading mechanism for efficient, resilient bulk writes into Gemini PostgreSQL.
We established data lineage tracking and processing history across the full pipeline, then handed off a system the client's team could monitor, extend, and trust — without depending on Renaiss to keep it running.
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