Netezza Performance Server (NPS) is an add-on data warehouse solution available on Cloud Pak for Data System platform, built over open source and optimized for High Performance Analytics with built-in hardware acceleration. Netezza Performance Server was previously named IBM Performance Server for PostgreSQL (IPS).
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.
We can query the data source and treat multiple databases as one with IBM Netezza Performance Server.
While delivering fast and reliable analytical performance, the IBM Netezza Performance Server requires minimal configuration and ongoing management.
To drive organizational performance, Netezza Performance Server automatically simplifies data and AI to centralize all analytics activities on the device, exactly where the data resides.
For data processing and application dashboards, IBM Netezza Performance Server is quite beneficial.
IBM Netezza Performance Server simplifies event setup by notifying you when a hardware component fails, allowing you to quickly replace it.
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.
Netezza is sufficient against similar products. It comes down to personal preference, I'd love to have the data objects popping up as I type but some people may not like it.
