Next-generation Advanced Data Warehouse and Analytics Platform Deployed On-Premises or On-Cloud

The NPS on Cloud Pak excels in scaling up resources in-place, significantly improving query concurrency and individual query performance. NPSaaS stands out as the premier choice for dynamic scaling up and down, adeptly adjusting resources to meet demands.

System-managed temporal tables retain updated and deleted rows for up to 92 days, equivalent to a quarter, ensuring comprehensive data tracking over an extended period. This feature of Cloud Pak for Netezza is pivotal for maintaining a detailed history of data changes, which is crucial for audit trails and data analysis.

Cloud Pak for Netezza can directly access Parquet files as external tables within an NPS data warehouse. This enables clients to benefit from cost-effective, easily scalable storage, and to query Parquet files in conjunction with data warehouse tables.

Scale your operations smoothly without juggling multiple data warehouse clusters. Enjoy the agility of precision prioritization, where shorter queries leap ahead for rapid execution. Benefit from consistent performance across all data warehouse operations. Optimize your database resources effortlessly, with smart workload management that adjusts to the needs of every query. 

Harness the power of Cloud Pak for Netezza with in-database analytics (INZA) for a wide array of functions including rule association, classification, regression, statistical analysis, and much more.

Furthermore, the introduction of the nzpyida package offers clients the capability to execute custom Machine Learning algorithms through an intuitive Python interface.

Leverage built-in data source support for various Netezza databases and external data sources. Utilize NPS alongside IBM Cloud Pak for Data (CP4D) and Data Virtualization (DV) to seamlessly integrate external data sources with NPS data. Employ NPS external tables, compatible with Parquet, CSV, and traditional data formats, to facilitate the joining of this data with data warehouse information.