Geospatial Data Processing at Scale

Today, we live in a world where geospatial data is pervasive, and the volume is increasing annually. From property insurance to financial applications, the need for efficient data processing poses a challenge. With Databricks, data scientists and data engineers can scale out geospatial data processing. 

Data Consistency 

The number of geospatial data providers has grown from a dozen in 1999, to over 200 in 2024. Many of the newer data providers utilize computer vision models to automate feature extraction from satellite imagery. Utilizing data from multiple providers requires data cleansing and normalization. Sometimes, it is necessary to combine data from multiple providers to fulfill the needs of risk models. Data consistency is critical to risk and predictive analysis. 

Data Formats 

The proliferation of data providers has increased the number of data formats. Managing gigabytes or terabytes of data in different formats is a burden. Ideally, applications read one common format. Standardizing the data format reduces code complexity, speeds up application development, standardizes the processing, and improves maintainability. When new data formats are introduced, the impact on software development, production systems and data promotion is minimized. 

Third Party Services and Libraries 

Today, there are numerous commercial and open-source tools for processing geospatial data. No single solution can solve every problem, and every vendor releases new features multiple times every year. Microsoft Azure, Google GCP, AWS, Apple, Apache Sedona, ArcGIS, OpenStreetMaps, and MapInfo are just a few examples of these tools. Learning multiple tools and keeping up-to-date can quickly become a burden. 

Installation and Best Practices 

Installing, configuring, and managing geospatial tools quickly becomes error-prone. When done manually, systems can break, and code promotion can fail catastrophically. Automating the download, installation, configuration and system start up can greatly reduce maintenance and improve quality.  

Conclusions 

Effective use of geospatial data can be a competitive and strategic advantage. If your company takes months or years to ingest, process, and utilize geospatial data, using Databricks can improve your process, reduce cost, and speed up time-to-market.

If your company would like to learn about geospatial applications on Databricks, contact us for help developing an approach to successful geospatial implementation on Databricks.