Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
If the environment has many different protocols for data exchange between partners and most of the protocols follow some standards like EDI X12, TIBCO BusinessConnect should be a good tool to take care this business use case. If the environment has a few simple protocols for data exchange, e.g. SFTP, FTPS or emails, some lighter weight tools will be enough and TIBCO BusinessConnect may be an overkill tool.
When a new version is released, its better to test it thoroughly before letting customers apply the release in their real time environment. There were missing functionalities from the previous versions which were not identified ahead of time.
If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.
The main reason TIBCO BusinessConnect was selected is because of the overall solution it provides with the different TIBCO products. Since TIBCO BusinessWorks and TIBCO Enterprise Message Service are already in place and the canonical model is already built and integrated into the Information Bus, TIBCO BusinessConnect is the best tool to work with them.
