NAPRTCS

Legacy Healthcare Data Migrated to Snowflake

NAPRTCS, a healthcare registry for children with kidney disease, had decades of patient data locked in SAS files incompatible with its new Snowflake environment. Ollon built a custom ETL pipeline that restructured the legacy dataset into SQL friendly tables and loaded it into Snowflake. NAPRTCS can now query its registry data directly in Snowflake, with documentation to extend the pipeline on its own.

Situation

NAPRTCS, a healthcare registry focused on children with kidney disease, needed to modernize its data infrastructure to support long term accessibility and analysis. Their dataset, originally stored in SAS format, was not compatible with their new cloud based Snowflake environment, creating a barrier to efficient data use. The organization required a full data migration strategy that ensured data integrity while enabling future scalability.

Solution

Ollon designed and executed a custom ETL process to extract, transform, and load the legacy data into Snowflake. The solution included restructuring the dataset into SQL friendly tables, optimizing for performance, and ensuring the data was clean and ready for downstream analysis. All components were implemented with cloud native practices, aligning with Snowflake’s architecture.

Results

With the data successfully migrated and structured in Snowflake, NAPRTCS gained immediate access to a secure and flexible environment for research and reporting. The transition reduced reliance on legacy formats and positioned the organization to scale its analytics capabilities with modern tooling. Ollon provided documentation and guidance to support future extensions and integrations as operational needs evolve.

Technologies

Python, Pandas, Snowflake Python SDK

Our Expertise in Action

ETL pipeline design and development

NAPRTCS' patient registry data lived in SAS files that had to be extracted, restructured, and loaded into Snowflake without losing any of the underlying research data. Ollon built a Python based pipeline that read the SAS files into dataframes, transformed each field to match a predetermined table schema, and pushed the results into Snowflake through its native data connector. Every transformation step was built to a schema designed before the pipeline was written, not improvised field by field as data appeared. That upfront design meant the loaded data was already structured for the SQL based queries NAPRTCS needed to run once the migration was complete.

Legacy system modernization

Decades of NAPRTCS' pediatric kidney disease registry data existed only in SAS format, a structure that worked for the statistical software it was built for but not for the SQL based analysis the organization wanted to move toward. Before writing any code, Ollon designed a table and database structure that could actually represent the data in Snowflake. That schema design work is what let years of legacy research data become queryable in a modern format instead of staying locked inside software built for a different era. NAPRTCS came out with a dataset structured for the analysis it actually wanted to do, not just a copy of the old format sitting in new storage.

Cloud migration

Moving a healthcare registry's data into the cloud meant more than copying files, since NAPRTCS needed the result to actually behave like a proper Snowflake database and not just an import sitting inside one. Ollon used secure, encrypted connections throughout the transfer to protect the underlying medical data, then optimized the final tables for the way Snowflake actually processes queries, not how the original SAS files had been organized. That cloud native structure gave NAPRTCS a scalable environment for research and reporting from the moment the migration finished. The registry now runs on infrastructure that can grow with its analytics needs instead of the fixed capacity of the original SAS setup.

Knowledge transfer, training, and documentation

NAPRTCS' own team was going to be the one running queries and extending the dataset long after the migration project ended, with no Ollon developer remaining on staff to answer questions about how the pipeline worked. Ollon wrote comprehensive documentation covering the full migration process, from the original schema design decisions through the transformation logic to the final Snowflake table structure. That documentation was handed off directly to NAPRTCS as part of closing out the engagement, giving the registry's team a reference for maintaining the data and building future integrations. The registry can now extend its own analytics work without needing to reverse engineer a pipeline nobody on staff originally built.