About this episode

Shailesh Kadam from Saks Global discusses the orchestration of retail data pipelines using Airflow and the transition to a cloud-native architecture.

Saks Global runs one of the largest retail data operations in the US, with around 8 million SKUs flowing across point-of-sale, e-commerce, catalog, and fraud detection systems into Snowflake. In this episode, [Shailesh Kadam]( linkedin.com ), Architect at [Saks Global]( saks.com ), joins Kenten to walk through how Airflow acts as the nervous system tying it all together, why they moved from self-managed Kubernetes to Astro, what is driving their Airflow 3 upgrade, and how they are approaching agentic AI, MCP, and credential security. Key Takeaways: 00:00 Introduction. 01:31 Saks Global today. Shailesh describes the business after separating e-commerce from brick and mortar and acquiring Neiman Marcus, and the modern cloud-native stack on AWS, Snowflake, and Airflow. 02:50 8 million SKUs in motion. Why every name, image, inventory, and price change has to flow in near real time across operational systems. 04:50 What the pipelines look like. Point-of-sale ingestion, fraud signals to third parties like Fiserv, and hourly product catalog feeds out to Meta and Google. 07:30 Moving off self-managed Kubernetes to Astro. Shailesh contrasts past experience with Kubernetes, IBM Tivoli, and…

People in this episode

Host: Kenten

Guest: Shailesh Kadam

Topics covered

• retail data operations
• data pipelines
• Airflow
• cloud-native stack
• agentic AI
• credential security

Keywords

• Saks Global
• Airflow
• data pipelines
• Snowflake
• Kubernetes
• Astro
• agentic AI
• MCP
• credential security

Mentioned in this episode

Organizations: Saks Global, Snowflake, Astro, AWS, Meta, Google, Fiserv, IBM Tivoli, Control-M

Products: Airflow, Airflow 3, LLM-driven product classification

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