Large scale geospatial Visualization using

S u c c e s s   S t o r y

Unleashing the Power of Geospatial Data Visualization with


Process Terabytes of Geospatial data and visualize it on



Technology Used

  • Programming Languages: Python
  • Data platform: Databricks
  • ELT: Delta Live Tables
  • Cloud: Azure
  • Cloud Storage: Azure Data Lake Gen 2
  • CI/CD: Github


The client is running a customer and employee reward program for its corporate client in the UAE region. The client is facing tough competition from its competitors and also facing internal data challenges. Data challenges faced in this scenario are multi-faceted. Firstly, processing terabytes of data requires a robust infrastructure and optimized data processing techniques. Second, integrating and harmonizing diverse data sources, such as structured, semi-structured, and unstructured data.

In addition to processing large volumes of data, there is a pressing need to achieve quick processing times. Lastly, the complex Extract, Load, Transform (ELT) process for geospatial data adds another layer of complexity. Geospatial data often requires preprocessing steps like spatial indexing, projection transformations, and feature extraction before it can be visualized effectively.


The Wolf of Data leverages the power of Databricks Delta Lakehouse architecture to tackle various data challenges. Firstly, the use of Delta Lake enables efficient handling of terabytes of data. Delta Lake provides a reliable and scalable storage layer that ensures data integrity, transactional consistency, and efficient query performance. By leveraging Delta Lake's capabilities, the Wolf of Data can process and analyze massive volumes of data seamlessly, enabling organizations to derive valuable insights from their data assets.

Wolf of Data utilizes the power of Apache Spark within the Databricks environment to handle structured and semi-structured data effortlessly. Spark's versatile and distributed computing framework allows for seamless processing of different data formats, enabling flexible data integration and transformation. With Spark's rich ecosystem of libraries and built-in support for various data sources, the Wolf of Data can easily work with diverse data types, performing complex operations and analytics to derive meaningful insights.

Moreover, the Wolf of Data optimizes Spark jobs to ensure that the entire data processing pipeline runs in less than an hour. By leveraging the distributed computing capabilities of Spark and employing performance tuning techniques, such as parallel processing and caching, the Wolf of Data achieves efficient and timely data processing. This allows organizations to obtain insights quickly, enabling faster decision-making and responsiveness to evolving business needs.

Additionally, the complexity of the Extract, Load, Transform (ELT) process is simplified through the use of Delta Live Tables pipelines. Delta Live Tables provides a streamlined and efficient approach to handle the ELT process for data pipelines. By leveraging the power of Delta Lake, combined with the ease of use and flexibility of Spark, the Wolf of Data can effectively manage and automate the data transformation workflows. This reduces the complexity and time required for data preparation, ensuring accurate and reliable data for visualization and analysis.

Impact we created

  • - Handle Terabytes of Data
  • - Saved more than 100K $ of Client's by using unified Data solution
  • - Developed Interactive Geospatial Analysis dashboard
  • - Allow client to see its competitor presence globally
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