How Godrej Properties Migrated Humungous Sales Data with InfraHive's AI-Powered Pipelines After Multiple Vendor Failures

Self-Learning AI Data Model enables unbreakable ETL/reverse ETL migration, eliminating 75% manual intervention

Who is Godrej Properties

Godrej Properties stands as one of India's premier real estate developers, delivering exceptional residential and commercial projects across major metropolitan cities. As part of the prestigious 125-year-old Godrej Group conglomerate, the company manages complex, large-scale property portfolios that demand rapid, data-driven decision making.

With operations spanning multiple specialized teams—IT & Analytics, Management, Design, and Finance—Godrej Properties required seamless data flow and real-time insights to maintain their competitive edge in India's dynamic real estate market.

Business Objective

Godrej Properties had one mission-critical objective: Execute a complex data migration of humungous sales data volumes from PowerBI to archive SQL Server for optimized long-term storage and enhanced reporting capabilities.

This enterprise-grade ETL and reverse ETL operation was essential to:

1. Preserve Historical Data: Safely archive years of invaluable sales data that had become trapped in PowerBI's limitations.
2. Enable Scalable Architecture: Transition to SQL Server for better performance, storage capacity, and data management as their property portfolio expanded.
3. Restore Automated Reporting: Establish seamless data pipelines that would feed clean, archived data back to PowerBI for continued dashboard and reporting functionality.
4. Eliminate Performance Bottlenecks: Move away from PowerBI's constraints on massive data volumes to a more robust, enterprise-ready infrastructure.

What was the Challenge

Godrej Properties faced a critical data migration crisis that threatened both their operational efficiency and data integrity.

1. Massive Data Migration Bottleneck: Humungous volumes of sales data trapped in PowerBI needed migration to archive SQL databases for long-term storage and automated reporting—a complex ETL and reverse ETL operation that standard tools couldn't handle.
2. Failed Internal & External Attempts: Both internal IT teams and external vendors had attempted the migration, but pipelines consistently broke under the massive data volumes. PowerBI's inherent limitations made large-scale data transfers extremely slow and unreliable with general migration tools.
3. Critical Data Loss Risk: Most alarmingly, the company faced the genuine risk of losing years of precious historical sales data if the migration wasn't executed properly—data that was irreplaceable and essential for trend analysis, forecasting, and compliance.
4. Cross-Departmental Gridlock: IT, Analytics, Management, Design, and Finance teams were all paralyzed, unable to access historical insights or generate reliable reports while the data remained trapped in an unsustainable PowerBI configuration.

Solutions & Results

InfraHive deployed a comprehensive data automation layer that transformed Godrej Properties' entire data workflow without disrupting their existing Microsoft PowerBI investment.

1. Enterprise-Grade Data Migration: InfraHive successfully executed the complex ETL and reverse ETL migration of humungous sales data volumes from PowerBI to archive SQL databases—a process that had defeated both internal IT teams and external vendors.
2. Unbreakable Pipeline Architecture: Unlike standard tools that broke under massive data loads, InfraHive's robust infrastructure handled PowerBI's large-scale data transfers seamlessly, ensuring zero data loss during the critical migration process.
3. Data Preservation Guarantee: Most critically, InfraHive protected years of irreplaceable historical sales data during migration, ensuring complete data integrity and continuity for trend analysis and compliance requirements.
4. Efficiency Breakthrough: 75% reduction in manual intervention across all data operations.
5. Unbreakable Data Pipelines: Robust ETL and reverse ETL processes powered by our AI Data Model that handle massive data volumes without the pipeline breaks that plagued previous solutions.