Data Analytics Portal

Doug

INDUSTRY

Real Estate

Type

Business Application

Overview & Challenges

Our client specialized in identifying real estate investment opportunities with a strong focus on the value derived from extensive data analysis. He identified multiple data sources that, if consolidated, transformed, connected, and analyzed, could provide significant insights. The primary challenge was the vast amount of data to be processed—ranging in terabytes, all packaged in numerous small zip files. The client required a sophisticated automation system to orchestrate these complex processes and needed a capable team to execute it.

Our Solution

We designed and developed an end-to-end solution to handle the entire data processing pipeline. This included automating the extraction of files from SFTP, unzipping them, validating and transforming the data, and loading it into a cloud-hosted MySQL database. We also orchestrated the entire process to ensure seamless and efficient operation.

Key Features and Benefits

Automated Data Extraction:

Implemented a system to automatically extract files from SFTP and unzip them, significantly reducing manual intervention.

Data Validation and Transformation:

Developed robust processes to validate and transform data, ensuring high-quality and consistent information.

Cloud-Hosted MySQL Database:

Utilized a MySQL RDS to store the vast amount of data, ensuring scalability and reliability.

Data Cleaning and Normalization:

Cleaned and normalized the data model to enhance its usability and accuracy for analytics.

Advanced Data Modeling:

Stored data in multiple formats suitable for analytics and KPI generation, enabling deeper insights.

Powerful Analytics:

Provided the client with powerful analytics capabilities to identify valuable insights and trends in the data.

Technologies Used

My SQL

Python

AWS

Implementation & Results

Our team utilized advanced technologies to build a scalable and efficient automation engine for ETL (Extract, Transform, Load) processes. The solution allowed the client to experience the full value of consolidated and cleaned data, enabling the identification of insights that were previously unattainable. This not only enhanced his investment strategies but also allowed him to provide data as a service to others.

Impact

Operational Efficiency:

Streamlined the data processing pipeline, significantly reducing the time and effort required for data extraction, transformation, and loading.

Enhanced Insights:

Enabled the client to uncover valuable insights from consolidated data, improving decision-making and investment strategies.

Data as a Service:

Allowed the client to monetize the cleaned and normalized data by offering it as a service to other interested parties.

Conclusion

The project successfully delivered a robust and scalable data processing solution, transforming the client’s ability to leverage data for real estate investment insights and creating new revenue opportunities through data as a service.