How Redica Transformed Their Data With Airflow and Snowflake with Shankar Mahindar

The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

The life sciences industry relies on data accuracy, regulatory insight and quality intelligence. Building a unified system that keeps these elements aligned is no small feat.

In this episode, we welcome Shankar Mahindar, Senior Data Engineer II at Redica Systems. We discuss how the team restructures its data platform with Airflow to strengthen governance, reduce compliance risk and improve customer experience.

Key Takeaways:

00:00 Introduction.

01:53 A focused analytics platform reduces compliance risk in life sciences.

07:31 A centralized warehouse orchestrated by Airflow strengthens governance.

09:12 Managed orchestration keeps attention on analytics and outcomes.

10:32 A modern transformation stack enables scalable modeling and operations.

11:51 Event-driven pipelines improve data freshness and responsiveness.

14:13 Asset-oriented scheduling and versioning enhance reliability and change control.

16:53 Observability and SLAs build confidence in data quality and freshness.

21:04 Priorities include partitioned assets and streamlined developer tooling.

Resources Mentioned:

Shankar Mahindar

https://www.linkedin.com/in/shankar-mahindar-83a61b137/

Redica Systems | LinkedIn

https://www.linkedin.com/company/redicasystems/

Redica Systems | Website

https://redica.com

Apache Airflow

https://airflow.apache.org/

Astronomer

https://www.astronomer.io/

Snowflake

https://www.snowflake.com/

AWS

https://aws.amazon.com/

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#AI #Automation #Airflow #MachineLearning