How to overcome key challenges in MuleSoft implementation

This blog post will discuss the top few challenges associated with implementing MuleSoft integration and how you can overcome them! 

Challenge 1: Data re-processing on each request

Solution: One common challenge when implementing MuleSoft system integration is data re-processing. This occurs when the data returned from the source system API calls must be continuously re-processed upon each request to formulate a response. While this may not seem like a big deal in small applications, it can become a problem when the application is scaled out.

This becomes an issue with MuleSoft system integration because each request has to go through all of the logic for any number of source systems before data can be returned or sent on for other purposes. As mentioned above, using APIs to merge continually and process data can slow down the entire system.

There are a few ways to mitigate this challenge:

• Use message brokers to buffer and multiplex requests between different systems. This will help keep the number of requests per second low and reduce each system’s load.
• Cache processed data instead of re-processing it. The team can use caching both at the message broker level and in MuleSoft data stores (e.g., Nexus). However, it is crucial to monitor whether or not caching causes any memory issues or if you run out of space when using this approach.
• Apply a layer of cache on top of all business logic. This can be used to bind data from multiple systems in a single MuleSoft request and cache the response.
• This approach would require you to write custom logic that interfaces with each source system’s API call, pulls up relevant information based on your requirements for caching, performs any additional transformations required by the business (e.g., aggregations), and finally caches the result. While this can be a lot of work, it may be the best option if data needs to be frequently accessed and re-processed.

Challenge 2: Complexity with duplicates

Solution: Another common challenge when implementing MuleSoft system integration is dealing with duplicates. Oftentimes, there are duplicate data records in different systems that need to be combined in order to generate a single response. This can add complexity and confusion when trying to manage the data.

There are several ways to deal with duplicates:

• Filter out duplicate data as it is being ingested into MuleSoft.
• Duplicate detection during runtime in MuleSoft.
• Duplicate detection during runtime using a separate tool, such as Solr or ElasticSearch.

This approach would require you to write custom logic that performs duplicate checks on the data being ingested and then trigger the actions above based on whether or not duplicates were found in your source system(s).

Challenge 3: Data proliferation

Solution: Another common challenge when implementing MuleSoft system integration is data proliferation. This occurs when the volume of data being processed and stored becomes too large, leading to performance issues and a greater chance for human error. As mentioned earlier, APIs can be used to merge and process data, leading to data proliferation continually.

There are a few ways to mitigate this challenge:

• Use message brokers to buffer and multiplex requests between different systems. This will help keep the number of requests per second low and reduce the load on each system.
• Cache processed data instead of re-processing it. Caching can be used both at the message broker level and in MuleSoft data stores (e.g., Nexus). However, it is important to monitor whether or not caching causes any memory issues or if you run out of space when using this approach.
• Apply a layer of cache on top of all business logic. This can be used to bind data from multiple systems in a single MuleSoft request and cache the response.
• This approach would require you to write custom logic that interfaces with each source system’s API call, pulls up relevant information based on your requirements for caching, performs any additional transformations required by the business (e.g., aggregations), and finally caches the result. While this can be a lot of work, it may be the best option if data needs to be frequently accessed and re-processed.

Challenge 4: Too many product releases in a short time

Solution: Implementing system integration with MuleSoft can also be challenging due to the number of product releases in a short amount of time. Trying to keep up with the product development cadence is a challenge in itself, and keeping systems compatible between releases can be even more of an issue (especially if new features require changes to existing APIs).

This approach would involve changing your data models as well as any custom logic that processes data based on how it was stored or processed in previous releases.

Challenge 5: Upgrade from MuleSoft X to MuleSoft Y— Would need to rewrite complete code in the new version.

Solution: Upgrading a system integration project can also be very challenging if the overall architecture of your solution is complex and requires extensive changes due to product upgrades or feature releases (e.g., MuleSoft X to MuleSoft Y). In this case, you would need to completely rewrite your code in the new version of MuleSoft.

This process can be time-consuming and challenging if a lot of the existing code is no longer compatible with the new product or release. It is also important to remember that any data models and custom logic you wrote would need to be rewritten as well.

This approach can also lead to data proliferation, which is a problem described earlier in this section.

Challenge 6: Batch processing and EDI

Solution: Another common challenge when implementing MuleSoft system integration is batch processing or dealing with Enterprise Data Interchange (EDI) files. These types of data often require specific handling and transformation techniques, which can be difficult to implement using the out-of-the-box connectors and components that are available in MuleSoft.

One way to handle this challenge is by using the MuleSoft Anypoint Platform, which provides a comprehensive batch processing framework that can be used to easily process these types of data. Additionally, the platform includes many pre-built connectors and adapters for EDI files that make it easy to get started.

Challenge 7: Licence cost

Solution: Another common challenge when implementing MuleSoft system integration is the cost of licenses. This can be a challenge because the cost of licenses for the Anypoint Platform can add up quickly, and this expense may not be immediately apparent when starting your project.

There are a few ways to mitigate this challenge. The first is to try and select the modules in MuleSoft that you think will be most useful for your project and avoid purchasing licenses for features or functionality that you do not plan to use. Additionally, it may be helpful to explore alternative license models such as subscription-based pricing, which can help spread out the cost of the overall project and make it easier to keep track of.

Overall, system integration implementation with MuleSoft can be a lot of work and requires some careful planning (especially when dealing with data transformation). However, by partnering with an experienced MuleSoft consulting services provider, you will be able to leverage your existing systems more effectively while saving time and money.

To have a clear assessment, talk to a Quinnox MuleSoft consultant today. Our MuleSoft consultants will give you a blueprint of what works currently in your current ecosystem and improvements that can be made.