Toptal streamlines data architecture for healthcare firm, cutting load times by 90%.
A leading dental healthcare company partnered with Toptal to resolve persistent ETL pipeline failures and data quality issues that were stalling critical business decisions.
Client
A Switzerland-based dental and medical device company providing implants and digital solutions for clinicians.
Employees
Revenue
Industry
Healthcare and Life SciencesDelivered Services
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Schedule a CallChallenge
The organization’s AWS Glue and Step Function-based pipelines frequently failed, leading to poor data quality and severe delays in decision-making across teams.
Solution
Multithreaded Data Refactor
Toptal overhauled Python-based ETL pipelines using multithreaded programming to eliminate timeouts, reshaped data in Snowflake and Athena, and improved performance with optimized SQL queries.
Architecture Simplification Strategy
Toptal replaced full data loads with incremental ones, implemented Snowflake and Athena external tables, and eliminated redundant bronze-silver-gold layers to streamline processing.
Outcome
Performance Time Reduced
Load times dropped from hours to minutes, and ETL reliability rose dramatically with pipeline uptime now exceeding 90%, restoring operational trust in analytics.
Scalability Foundation Built
The updated architecture enabled faster pipeline deployment, simplified maintenance, and established a strong foundation for long-term scalability and monitoring improvements.
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