EPISODE · Jul 16, 2026 · 25 MIN
Orchestrating Retail Data Pipelines at Saks Global
from The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI · host Astronomer
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 Control-M against running on Astro.09:35 Upgrading to Airflow 3. Event and asset-based scheduling, DAG versioning, task isolation, and using Otto to convert DAGs in a phased rollout.13:13 Agentic AI and MCP on the roadmap. How Saks plans to use Airflow's MCP for LLM-driven product classification and to feed Snowflake analyses like churn and spend.18:01 Securing PII and credentials. Secrets backends, cloud secret manager integration, key rotation, and keeping credentials out of DAG code.21:02 Wishlist for Airflow. Interactive data lineage across DAGs and a UI-based debugging interface for support teams.Resources Mentioned:[Apache Airflow](airflow.apache.org)[Astro](astronomer.io/product)[Otto, the Astronomer data engineering agent](astronomer.io)[Snowflake](snowflake.com)[Saks Global](saks.com)Thanks for listening to "The Data Flowcast: Mastering Apache Airflow® for Data Engineering and AI." If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations.#AI #Automation #Airflow
What this episode covers
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 Control-M against running on Astro.09:35 Upgrading to Airflow 3. Event and asset-based scheduling, DAG versioning, task isolation, and using Otto to convert DAGs in a phased rollout.13:13 Agentic AI and MCP on the roadmap. How Saks plans to use Airflow's MCP for LLM-driven product classification and to feed Snowflake analyses like churn and spend.18:01 Securing PII and credentials. Secrets backends, cloud secret manager integration, key rotation, and keeping credentials out of DAG code.21:02 Wishlist for Airflow. Interactive data lineage across DAGs and a UI-based debugging interface for support teams.Resources Mentioned:[Apache Airflow](airflow.apache.org)[Astro](astronomer.io/product)[Otto, the Astronomer data engineering agent](astronomer.io)[Snowflake](snowflake.com)[Saks Global](saks.com)Thanks for listening to "The Data Flowcast: Mastering Apache Airflow® for Data Engineering and AI." If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations.#AI #Automation #Airflow
NOW PLAYING
Orchestrating Retail Data Pipelines at Saks Global
No transcript for this episode yet
Similar Episodes
Mar 26, 2026 ·1m
Mar 19, 2026 ·34m
Feb 18, 2026 ·11m
Feb 11, 2026 ·45m