Databricks

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With origins in academia and the open source community, Databricks was founded in 2013 by the original creators of Apache Spark™, Delta Lake and MLflow. As the world’s first and only lakehouse platform in the cloud, Databricks combines the best of data warehouses and data lakes to offer an open and unified platform for data and AI.

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Optimizing Order Picking to Increase Omnichannel Profitability with Databricks

Date:

Authors: Peyman Mohajerian, Bryan Smith

Topics: BOPIS, Operations Research

Organizations: Databricks

The core challenge most retailers are facing today is not how to deliver goods to customers in a timely manner, but how to do so while retaining profitability. It is estimated that margins are reduced 3 to 8 percentage-points on each order placed online for rapid fulfillment. The cost of sending a worker to store shelves to pick the items for each order is the primary culprit, and with the cost of labor only rising (and customers expressing little interest in paying a premium for what are increasingly seen as baseline services), retailers are feeling squeezed.

But by parallelizing the work, the days or even weeks often spent evaluating an approach can be reduced to hours or even minutes. The key is to identify discrete, independent units of work within the larger evaluation set and then to leverage technology to distribute these across a large, computational infrastructure. In the picking optimization explored above, each order represents such a unit of work as the sequencing of the items in one order has no impact on the sequencing of any others. At the extreme end of things, we might execute optimizations on all 3.3-millions simultaneously to perform our work incredibly quickly.

Read more at Databricks Blog

How to Build Scalable Data and AI Industrial IoT Solutions in Manufacturing

Date:

Authors: Bala Amavasai, Vamsi Krishna Bhupasamudram, Ashwin Voorakkara

Organizations: Databricks, Tredence

Unlike traditional data architectures, which are IT-based, in manufacturing there is an intersection between hardware and software that requires an OT (operational technology) architecture. OT has to contend with processes and physical machinery. Each component and aspect of this architecture is designed to address a specific need or challenge, when dealing with industrial operations.

The Databricks Lakehouse Platform is ideally suited to manage large amounts of streaming data. Built on the foundation of Delta Lake, you can work with the large quantities of data streams delivered in small chunks from these multiple sensors and devices, providing ACID compliances and eliminating job failures compared to traditional warehouse architectures. The Lakehouse platform is designed to scale with large data volumes. Manufacturing produces multiple data types consisting of semi-structured (JSON, XML, MQTT, etc.) or unstructured (video, audio, PDF, etc.), which the platform pattern fully supports. By merging all these data types onto one platform, only one version of the truth exists, leading to more accurate outcomes.

Read more at Databricks Blog