I was missing some features and scalability from the built-in InRiver iPMC extension log. Therefore I did an integration that demonstrates how to log events, exceptions and metrics to Azure Application Insights (using it as a supplementary log service).
I created an open sourced inbound connector to demonstrate how to make a simple, yet powerful, integration for loading large XML documents with product data into iPMC. After doing thorough profiling of the code, I also incorporated several performance optimizations.
What if product managers wants to have all images tagged in InRiver, but does not really like to do it manually? How can that be automated using artificial intelligence in the cloud? In this blog post I create a new InRiver iPMC extension that integrates to Azure's Computer Vision API.
In InRiver some fields have raw values that need to be transformed because of a target system. It could be that all color codes should be consolidated to just 20 color groups. But how to do that? And where to store all the transformation combinations? In this blog post, I demonstrate how to implement an InRiver iPMC extension that transforms field values using a custom table in Azure Table Storage.
From working on InRiver PIM projects, I have implemented a few custom outbound connectors. They would export catalogs to EPiServer Commerce, Demandware (now Salesforce Commerce Cloud) as well as Azure Blob Storage. In this post I give away six tips on implementing a good outbound connector for InRiver Product Marketing Cloud (iPMC).