Michael Jones

Processing data in an Enterprise environment frequently involves very large files. In a traditional approach, processing these files can involve issues around memory and time as it involves first downloading the entire file, attempting to load the entire file into memory to work on, and then saving the whole file to output. Instead of taking this old-fashioned approach, we can utilized data streaming and deferred DataWeave processing in order to streamline this process.

Translating MQL for use with the MongoDB connector can be confusing. In this post I'm going to attempt to explain several common use cases and how to implement them with the Mules 4 MongoDB Connector. Hopefully by the end you will also have a better idea about the thought process that goes into making the translation.

When building APIs, we typically provide the ability to sort or filter data on GET operations via query parameters. While building basic query parameters in MuleSoft is simple, expanding your API to support optional and repeatable parameters can seem daunting when you're new to the platform. We will be building a simplistic Product System API in order to demo optional and repeatable query params. To accomplish this, we will be building dynamic (parameterized) SQL, and as a bonus wrapping this functionality into a reusable library.