Message brokering across different systems, with transactionality and the ability to have fine tuned control over what happens using Java (or other languages), instead of a heavy, proprietary languages. One situation that it doesn't fit very well (as far as I have experienced) is when your workflow requires significant data mapping. While possible when using Java tooling, some other visual data mapping tools in other integration frameworks are easier to work with.
If you’re bringing anything into Salesforce you should just invest now into Mule, you will get your money’s worth and find a myriad of uses to build APIs between many other systems. Once you build a component you can easily reuse it as a building block to attach to another source/destination. This makes it easy to ramp up quickly and spread usage of Mule throughout your enterprise. A good value for medium to large companies, but probably cheaper to outsource your job to a consulting firm if you are smaller.
As I mentioned earlier SQL Server Integration Services is suitable if you want to manage data from different applications. It really helps in fetching the data and generating reports. Its automation make it very easy and time efficient. It works well with large database as well. But it doesn't work well with real time data, it will take some time to gather the real time data. I would not recommend using it in a real time/fast-paced environment.
Camel has an easy learning curve. It is fairly well documented and there are about 5-6 books on Camel.
There is a large user group and blogs devoted to all things Camel and the developers of Camel provide quick answers and have also been very quick to patch Camel, when bugs are reported.
Camel integrates well with well known frameworks like Spring, and other middleware products like Apache Karaf and Servicemix.
There are over 150 components for the Camel framework that help integrate with diverse software platforms.
It is best suited for Rest API development. Mule ESB uses RAML as an API descriptor which is less complex and easy to understand. RAML is an open standard majorly supported by Mulesoft. Once RAML is developed, it is very easy (a few clicks)to create flows corresponding to the resources defined in the RAML. One can also include JSON schema validation in RAML, and with the use of APIkit router, Mule ESB makes the request validation very easy (it's automatic basically.)
Mule ESB comes with a large spectrum of community and enterprise connectors. We have connectors for all the major platforms like Facebook, Twitter, Salesforce, SAP, etc. This enables Mule ESB to integrate with the other systems in a faster and more robust way. Mule ESB has many components to fulfill the requirements of each integration (for example batch processing, parallel processing, choice, etc.)
Mule API gateway is one of the best tools (modules) of Mulesoft's offering. It supports API governance and management very well. One can easily enforce policies on their APIs with API gateway. It enables some of the must-have features in an API solution (i.e. throttling, oAuth, access levels, etc.)
Implementing a CI/CD (DevOps) environment for Mule ESB is a very easy task. Mule majorly uses MAVEN as its build tool, which in turn makes it best suitable for CI/CD approach. Mule also provides MAVEN plugins for auto deployments to the servers. Mule also has a best Unit testing module which is MUnit. MUnit can be used for both Unit and Functional testing, and it is easy to write and generates coverage reports in various formats.
Connection managers for online data sources can be tricky to configure.
Performance tuning is an art form and trialing different data flow task options can be cumbersome. SSIS can do a better job of providing performance data including historical for monitoring.
Mapping destination using OLE DB command is difficult as destination columns are unnamed.
Excel or flat file connections are limited by version and type.
Some features should be revised or improved, some tools (using it with Visual Studio) of the toolbox should be less schematic and somewhat more flexible. Using for example, the CSV data import is still very old-fashioned and if the data format changes it requires a bit of manual labor to accept the new data structure
SSIS is a great tool for most ETL needs. It has the 90% (or more) use cases covered and even in many of the use cases where it is not ideal SSIS can be extended via a .NET language to do the job well in a supportable way for almost any performance workload.
SQL Server Integration Services performance is dependent directly upon the resources provided to the system. In our environment, we allocated 6 nodes of 4 CPUs, 64GB each, running in parallel. Unfortunately, we had to ramp-up to such a robust environment to get the performance to where we needed it. Most of the reports are completed in a reasonable timeframe. However, in the case of slow running reports, it is often difficult if not impossible to cancel the report without killing the report instance or stopping the service.
The support, when necessary, is excellent. But beyond that, it is very rarely necessary because the user community is so large, vibrant and knowledgable, a simple Google query or forum question can answer almost everything you want to know. You can also get prewritten script tasks with a variety of functionality that saves a lot of time.
The implementation may be different in each case, it is important to properly analyze all the existing infrastructure to understand the kind of work needed, the type of software used and the compatibility between these, the features that you want to exploit, to understand what is possible and which ones require integration with third-party tools
If you are looking for a Java-based open source low cost equivalent to webMethods or Azure Logic Apps, Apache Camel is an excellent choice as it is mature and widely deployed, and included in many vendored Java application servers too such as Redhat JBoss EAP. Apache Camel is lacking on the GUI tooling side compared to commercial products such as webMethods or Azure Logic Apps.
I think SQL Server Integration Services is better suited for on-premises data movement and ADF is more suited for the cloud. Though ADF has more connectors, SQL Server Integration Services is more robust and has better functionality just because it has been around much longer
Very fast time to market in that so many components are available to use immediately.
Error handling mechanisms and patterns of practice are robust and easy to use which in turn has made our application more robust from the start, so fewer bugs.
However, testing and debugging routes is more challenging than working is standard Java so that takes more time (less time than writing the components from scratch).
Most people don't know Camel coming in and many junior developers find it overwhelming and are not enthusiastic to learn it. So finding people that want to develop/maintain it is a challenge.
Without this, we would have to manually update a spreadsheet of our SQL Server inventory
We would also have poor alerting; if an instance was down we wouldn't know until it was reported by a user
We only have one other person who uses SQL Server Integration Services , he's the expert. It would fall to me without him and I would not enjoy being responsible for it.
