In the world of data integration, error handling is crucial to a successful data integration strategy, however, it is often one of the most overlooked areas because, quite frankly, it can be quite intimidating when you initially try and understand it. In my series of blog posts, I hope to reduce this complexity by breaking it up into simple concepts that are easier to digest.
It’s a new year and people are re-inventing themselves with their new year resolutions. We can apply this same concept to other aspects of our days, such as the way we integrate systems! Here are five things that you should start (or stop) doing when it comes to data integration in 2020.
Often times when you are processing data through a flow, you may want to treat certain errors differently than others. For instance, if you are trying to select records from Salesforce, you would want to handle a record not found error differently than an out of memory error. For this reason, MuleSoft allows us to handle errors based on use cases as well as the types of errors that are being thrown.
In my previous blog post, I discussed the basics of error handling with Mule 4, helped understand what a Mule error is, what the two major error handling scopes in Mule 4 are, as well as how they work. In this post, I will discuss how to take these basic concepts and build them up so that you can implement error handling strategies in your application (and not be completely lost when doing so).
Like many developers and architects who build APIs and integrations, I was on top of the world when I completed the training on Anypoint Platform Development fundamentals (Mule 4); I was now able to take an idea for an API and build, design, deploy, and implement my API in a matter of hours. I now held the shiny key to become a MuleSoft Certified Developer — I just had to pass the MuleSoft Certified Developer –
