Dask Use Case | Who Uses Dask: WalMart

Walmart is using Dask to develop forecasts for all of their items in all of their stores. So Walmart has an incredible number of items in their catalog and an incredible number of stores, and they want to make sure that every item is in-stock at every store to the extent that it should be. This is just a huge computational problem. Using libraries like rapids and xgboost, Walmart has been able to accelerate, you know, 100 fold in some cases.

Now, if you're not Walmart, but you still want that kind of logistics, you might use a product for a company like Blue Yonder, previously called JDA. Blue Yonder uses Dask all over the place and it powers their ETL pipelines.

They process terabytes of data daily and they really like that Dask can scale elastically. That keeps things efficient for them. It keeps things cheap while also being very reliable. So Blue Yonder runs hundreds of Dask clusters in production every day. And Sebastian Neubauer at Blue Yonder says that "It's important that data scientists are able to deploy code directly into production. This keeps feedback cycles short and waste low."

So the fact that they can write pandas like code with Dask while they're experimenting and then push that directly to production to be actually in critical workloads is really valuable to them and that saves them a lot of money.

What is Dask?

Dask is a free and open-source library for parallel computing in Python. Dask is a community project maintained by developers and organizations.

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KEY MOMENTS
00:00 Dask in Retail
00:12 Walmart Demand Forecasting
00:25 Dask + Rapids + XGBoost
00:33 Blue Yonder Logistics
01:02 Data Scientists Can Deploy Code Directly Into Production