Revolutionizing Retail With IoT for Omnichannel Success

ICYMI: Think about the last thing you bought. How important was technology to the purchase process? Perhaps you did some research online to compare specs or prices, before buying in store. But, more likely than not, a traditional brick and mortar store didn’t factor into your decision at all. More and more purchases today are taking place online, or increasingly from directly within a retailer’s mobile app. Let’s think about how that changes the customer relationship and the role of the retailer in our societies.

When the web and mobile technologies first became accessible to retailers, they invested significant time and resources to drive consumers to these channels. Technology attempted to make online purchases as seamless as possible and to recreate the in-store experience online. Ironically, traditional retailers now face the opposite problem: attempting to drive traffic back into stores by providing an experience that surpasses the convenience and breadth of the online marketplace. https://goo.gl/DiY6q6 #DataIntegration #ML

DataWeave – Tip #3

I have already presented how to call Java code using message processors with the newest Java Module for Mule 4.x. For earlier Mule versions, Entry Point Resolvers were used to invoke custom Java code. However, for scenarios when we would like to use custom code in DataWeave, for transformations, another approach is needed. The approach presented in this article will be more concise, comparing ways in which we can use message processors. This process has been highly extended, compared to the possibilities of Mule 3.x. Mule 3 allowed us to invoke static methods. In contrast, Mule 4 not only permits us to call static methods but also instantiate classes and access their instance attributes.

Invoke a Method

Below, I have attached the DataWeave code responsible for converting an array into a list. To perform the conversion, I will use the already existing asList static method in the Arrays class. https://goo.gl/tkKsFD #DataIntegration #ML