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Data fabric tutorials
Data fabric tutorials

Data fabric tutorials

Take 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.


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