Data fabric tutorials
Complete data fabric tutorials to experience one or more of the use cases that combine to demonstrate how you can implement a data fabric solution.
Tutorial scenarios
Golden Bank is a leading mortgage provider through their network of neighborhood branches. The tutorials cover these goals:
- The bank uses AI to process loan applications and needs to avoid unanticipated risk and ensure that its applicants are being treated fairly.
- Based on a new regulation, the bank cannot lend to underqualified loan applicants. The bank needs a data pipeline that delivers concise, pre-processed, and up-to-date data on all mortgage applicants, so that lenders can make decisions. The existing data is in three data sources: a Db2 Warehouse, a PostgreSQL database, and a MongoDB database. The bank needs to integrate the data without moving it, and then transform the data into a single target data set.
- The bank wants to run a campaign to offer lower mortgage rates. The bank needs a consolidated 360 view of applicants to identify the highest value customers to target and help to determine the best rates to offer them.
- The bank has several departments that need access to high-quality customer and mortgage data. The bank needs to create a business vocabulary to describe and manage data assets, and then provide high-quality data assets that their data scientists can easily find in a self-service catalog.
Tutorials
Each of these tutorials is associated with resources in the Gallery and provides the full instructions for completing the tutorial.
Use case | Tutorial | Description | Expertise for tutorial |
---|---|---|---|
MLOps and trustworthy AI | Build and deploy a model | Train a model, promote it to a deployment space, and deploy the model. | Run a notebook. |
MLOps and trustworthy AI | Test and validate the model | Evaluate a model for accuracy, fairness, and explainability. | Run a notebook, and view results in user interface. |
Multicloud data integration | Integrate data | Extract, filter, join, and transform your data. | Use the DataStage drag and drop interface to transform data. |
Customer 360 | Configure a 360-degree view | Set up, map, and model your data to create a 360-degree view of your customers. | Use the Match 360 drag and drop interface to configure your 360 view. |
Customer 360 | Explore your customers | Explore the 360-degree view to identify the best customers for the marketing campaign offers. | Use the Match 360 drag and drop interface to explore data. |
Data governance and privacy | Trust your data | Create trusted data assets by enriching your data and running data quality analysis. | Run the Metadata import and Metadata enrichment tools. |
Data governance and privacy | Protect your data | Control access to data across Cloud Pak for Data as a Service. | Create data protection rules. |
Data governance and privacy | Know your data | Evaluate, share, shape, and analyze data. | Explore a catalog and run the Data Refinery tool. |
Learn more
Parent topic: Getting started