Tag: Data Engineering
All the articles with the tag "Data Engineering".
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From raw claims to RAF: what the data pipeline actually looks like
The path from a raw Medicare claim file to a patient's Risk Adjustment Factor score involves five distinct transformation layers. This is what each layer does and where the dbt models fit in.
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HCC suspecting explained from a data engineering perspective
HCC suspecting is about identifying conditions documented in prior years that haven't appeared in claims yet this year. This is what the data pipeline looks like and what the Tuva mart actually produces.
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Running the Tuva Project on DuckDB — what breaks and how to fix it
Tuva 0.17.2 on DuckDB 1.10 with dbt 1.11 produces three distinct failure modes. This is what actually broke and how each one was fixed.
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What 167k synthetic Medicare claims taught me about US healthcare data
Running the Tuva Project on 167k synthetic Medicare claims reveals the structural complexity of US healthcare data — before you've dealt with a single real data quality issue.
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What is the Tuva Project and why should data engineers care
The Tuva Project is an open-source dbt package that transforms raw healthcare claims into analytics-ready mart tables covering HEDIS, HCC, CCSR, readmissions, and data quality. This is what it actually does and why it matters.
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Lambda vs n8n: A Simple Explanation for Data Workflows
Introduction When building data systems or integrating APIs, a common question appears: should we use AWS Lambda or n8n? Both tools can automate processes, call APIs, and move data between systems,
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Should You Use AWS Lambda or AWS Glue to Update Records in HubSpot?
When integrating HubSpot with a data platform on AWS, a common architectural decision appears quickly: Should updates to HubSpot be executed from AWS Lambda or AWS Glue? The correct choice depends on
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What Is a Data Lake and What Is a Data Lakehouse?
Over the last decade, the world of data architecture has gone through several transformations. From traditional data warehouses to Hadoop-based data lakes and now to the emerging Lakehouse paradigm,