Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
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IBM Cloud Pak for Data
Score 8.1 out of 10
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IBM Cloud Pak for Data (formerly IBM Cloud Private for Data) provides data management, data governance, and automated data discovery and classification.
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.
IBM Cloud Pak for Data with Netezza is well suited for clients who require fast, economical analytics processing. It is not designed to be used as a transactional processing environment. For example, a large customer is using it during the point of sale process. That makes little sense in that business case. However, to take analysis to market faster, it excels well in that space.
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.
IBM Cloud Pak for Data takes the IBM Cognos solution and provides this on an enterprise cloud platform that can be extended to support better data integration and data science capabilities.
