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.
Does data compression very well, produces query results very quickly, ability to scale up as data volume/size grow in an organization, row level versioning is in memory and hence the speed. It is a very stable product for large enterprises. More detailed documentation on how to use this product in the initial stages would be really welcome.
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.
SAP IQ support is top-notch. I prefer starting all my SAP IQ support tickets with their Instant Messenger, where the majority of our issues are resolved. If it makes it to their ticketing system, they are very prompt at responding and very knowledgeable in the platform.
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.
SAP IQ is perhaps an under-marketed product in the sense that it is able to scale up very well and perform much faster and more efficiently than other products such as Oracle - when we ran multiple large queries on both Sybase IQ and Oracle, we found that that results were much faster in Sybase IQ and this gave the confidence to go for this product.
