/ Case Studies / Healthcare & Life Sciences / Blue Yonder: Handling Millions of Records for a Digital Supply Chain Solution

Blue Yonder: Handling Millions of Records for a Digital Supply Chain Solution

Fast-tracking onboarding of new customers and providing Data-as-a-Service

With the rise of online B2B activities, digital Supply Chain Management (SCM) solutions have more ground to cover than ever before. Coupled with terabytes of data generated by each client, organizations now have to record, process, and analyze high volume sets of data at rapid scale.
SCM solutions now highly depend on the stability of their underlying data stack.

Customer Background

Blue Yonder is the world leader in digital supply chain and omnichannel commerce fulfillment. Their intelligent, end-to-end platform enables retailers, manufacturers, and logistics providers to seamlessly predict, pivot and fulfill customer demand. With Blue Yonder, clients can automate processes that empower profitable business decisions, drive greater growth, and re-imagine customer experiences.

As the business scaled, however, Blue Yonder’s existing data pipeline attempted to process millions of internal and customer data records into and out of systems. This led to lengthy data load times between data transfers, causing delays in processing and poor data accessibility for internal and external stakeholders. Underlying systems were incapable of handling the load size and led to poor data accessibility. The inability to quickly access and process large amounts of data hindered the company’s ability to make data-driven decisions and slowed the pace of innovation.

Embrace the Future of Supply Chain Management followed a four-stage approach to fast-track data availability for sharing-as-a-service. The iterative process helped to create a data pipeline framework that focused on continuous data ingestion, cleansing, and modeling.

Understanding the business focuses

Blue Yonder’s SCM solution covers everything from Supply Chain planning to execution. The team worked on understanding the focus and niche around Supply Chain execution with Warehouse Management. The SCM solution required a scalable and performant underlying data storage system.

Centralizing everything in Snowflake Cloud Data Platform

The next step was to consolidate Blue Yonder’s data into the Snowflake Data Cloud. Snowflake provides a single platform with an elastic performance engine. Data from all Snowflake accounts was loaded into a single account followed by data from other sources being ingested and processed in real-time. For future data analytics workloads, the data analytics architecture was revamped so that each data processing unit could run independently of the others. The focus was to create resilient and governed data pipelines.

Enabling effective Data Consumption & Data Retrieval

Once the data was available in the underlying data warehouse, the next task was to make the data available on-demand, near real-time. Snowflake Data Cloud was powering the front end Data Apps
and hence the pipelines were designed keeping performance as an important factor.

Cost Monitoring & Optimization

In addition, Blue Yonder looked to optimize costs within Snowflake and reduce operational overhead, without significant downtime. The and Snowflake teams worked together to design a cost-tracking setup tailored to Blue Yonder’s specific requirements. As a result, cost tracking enhancements were achieved with the following:

  • Introduction of Tagging policies on resources
  • Automation of Cost tracking dashboard
  • Warehouse and Workload Monitoring Dashboard


With the solutions developed by the team, Blue Yonder can now launch a Snowflake environment for any new team within 15 minutes. This is down from two hours in previous provisioning requirements.

Additionally, Blue Yonder has a fully revamped data analytics architecture which has reduced the time to process and transform data to 1/10th of previous requirements.

The solutions further automated real-time data validation at a microscopic level and optimized the onboarding of new teams. Combined with Snowflake’s data sharing feature, Data-as-a Service (DaaS) was implemented for Blue Yonder customers and brought the total onboarding time required down from 5 hours to 15 minutes.

The implementation of monitoring and cost-tracking mechanisms significantly improves the budgeting process of the data warehouse utilization within Blue Yonder, and enables more effective resource allocation based on team need.

Patient 360
  • Snowflake
  • Azure
  • Azure Data Factory
  • SQL Server
The Future of Logistics Today.

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January 23, 2024