Problem Statement

The client, a large real estate firm, struggled to manage the increasing complexity of their data. With information coming from multiple sources, they needed a scalable solution to consolidate and process large volumes of financial and property data. The lack of an efficient system also made it difficult to derive insights for decision-making.

Approach & Solution

To address these challenges, we built a comprehensive data integration system on Google Cloud Platform, designed for scalability and performance.

We created an automated ETL pipeline to consolidate over 2 million data entries from sources like Salesforce API and Google Sheets, ensuring data was properly transformed and cleansed before entering BigQuery.
Leveraging Python scripts, we automated the ingestion and transformation process, improving data accuracy and efficiency.
All relevant data was centralized in BigQuery, allowing for seamless data retrieval and analysis across the organization.
Custom Looker Studio dashboards were built to provide real-time insights into key financial and property metrics, enabling better decision-making for executives.

Results & Outcomes

By automating the ETL processes, the client saved 50 hours per week previously spent on manual data processing. The centralized data system enabled quicker access to critical information, facilitating more informed decision-making. Overall, the streamlined operations reduced errors and saved costs on manual data entry efforts.