Howard Hughes
Global real estate developer cut ETL time by 80%, integrated new data sources, and reduced data warehouse costs by 83% with Boomi.
Business goals
Howard Hughes is a leading real estate developer managing communities for over 387,000 residents across six regions. To its large-scale planning, sales, and financial operations, the company wanted to simplify its complex, fragmented data stack, improve data ingestion and transformation speed, and enable new data integrations to support more accurate forecasting and financial automation. Another goal was to enable its in-house data team to build reliable, usable data pipelines and dashboards for business users, reducing operational costs and dependency on external consultants.
Integration Challenges
Howard Hughes faced multiple integration challenges that hindered operational efficiency and scalability:
- A complex legacy stack with overlapping tools (Talend, dbt, Jenkins, MySQL) that was difficult to maintain
- Long ETL windows of up to 12 hours daily causing delays and manual error handling
- Multiple external consultants were required, increasing costs and complexity
- Incomplete data integration prevented comprehensive analytics
- Inefficient batch loading to Snowflake inflated cloud warehouse costs
How Boomi Helped
Howard Hughes adopted Boomi Data Integration to unify data ingestion, transformation, and orchestration on a single platform. Key deployments included:
- Change Data Capture (CDC) replication for SQL Server to Snowflake, reducing load times dramatically
- Migration of SQL-based transformations from dbt into Boomi’s push-down ELT rivers
- Integration of previously disconnected real estate systems (Chatham, Blackline) using Boomi’s no-code and Python-managed custom connectors
- Empowering the in-house data team of two to manage pipelines previously requiring six external consultants
Results
By consolidating its data stack with Boomi, Howard Hughes achieved:
- ETL time cut from 12 hours to under 40 minutes, increasing agility
- $300K annual savings by eliminating external data engineering consultants
- 83% reduction in Snowflake costs through CDC-based incremental data loads
- Addition of three new data sources enabling automated sales forecasts and financial process automation
- Overall 60–70% reduction in data architecture costs, freeing the team to focus on business-critical analytics
