In this developer code pattern, you will learn how to load data from various sources and access it to perform research or other business-related activities using Netezza® Performance Server. The parallel architecture of the Netezza database environment enables high-performance computation on large data sets, making it the ideal platform for large-scale data mining applications.
We will use energy price data and an Australian weather station dataset to analyze the data using Jupyter Notebook with IBM Cloud Pak™ for Data. We will step through:
- Connecting to the Netezza database
- Loading data to Netezza using a CSV file, external table, and Cloud Object Storage
- Analyzing and visualizing the data loaded from Netezza Performance Server
- User loads Jupyter Notebook into IBM Cloud Pak for Data.
- User connects to Netezza using nzpy library connector.
- User loads and analyzes data from Netezza Performance Server.
Please see the README for detailed instructions on how to:
- Create a new project in CP4D
- Add connection to Netezza Performance Server
- Load notebook to your project
- Install nzpy library
- Configure Netezza Performance Server connection in notebook
- Load or unload data from external source
- Load data from other data sources
- Load data from Cloud Object Storage
- Load and analyze Australian weather station data
Try the next part of this learning path below.