Data Processing

Scalable and Simple Data Processing

Processing large volumes of healthcare data is challenging. While processing a few thousand records in a database is relatively straightforward, scaling this to hundreds of thousands, millions, or even billions of records is non-trivial.

Apache Spark is widely used for building scalable data processing pipelines, but it can be difficult to apply to healthcare data, especially nested JSON formats like FHIR. It typically requires specialized data engineers, who are both hard to find and expensive.

The b.well Open Source SDK simplifies this process by providing an easy-to-use layer on top of Spark and a set of Spark components designed for healthcare data, particularly FHIR. This allows clients to build scalable data processing solutions without the inherent complexity of Apache Spark, making it accessible to a broader range of data engineers.