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 with Cloud Pak for Data as a Service. The tutorials follow the story of Golden Bank, a leading mortgage provider, that needs to solve the challenges of data access, data quality, data governance, and managing data and AI lifecycles.
A data fabric is an architecture that provides a secure and consistent way to access data from disparate sources and a set of integrated tools so that your organization can efficiently collaborate to use your data to improve your business.
The data fabric is split into four use cases that each represent a particular goal. On Cloud Pak for Data as a Service, services provide features and tools. Each use case requires one or more service instances. Some services are included in multiple use cases.
Tutorials
The tutorials are grouped by use case. You can start with any use case. Each group of tutorials is based on a sample project that contains the resources that you need to complete the tutorials. You import the sample projects from the Gallery.
The tags for each tutorial describe the level of expertise (, , or ), the amount of coding required ( or ), and whether the tutorial is a continuation () of one or more other tutorials that you must complete first.
AI governance
Build, operationalize, and govern AI.
Scenario: Golden Bank needs a model that identifies whether customers qualify for mortgages to reduce the bank's application processing costs.
Click a tutorial for this use case to get started:
- Build and deploy a model
Train a model, promote it to a deployment space, and deploy the model. See instructions
- Test and validate the model
Evaluate a model for accuracy, fairness, and explainability. See instructions
Data Science and MLOps
Build, deploy, and monitor models.
Scenario: Golden Bank needs to automate a data pipeline that delivers up-to-date data on all mortgage applicants, that lends can use for decision making.
Click a tutorial for this use case to get started:
- Orchestrate an AI pipeline with model monitoring
Create an end-to-end pipeline that prepares data, trains a model, and then validate the model. See instructions
- Orchestrate an AI pipeline with data integration
Create an end-to-end pipeline that transforms data and trains a model. See instructions
Data integration
Provide access to all your data, without moving it.
Scenario: Golden 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.
Click a tutorial for this use case to get started:
- Integrate data
Extract, filter, join, and transform your data. See instructions
- Virtualize external data
Virtualize and join data tables from external sources. See instructions
- Replicate data
Set up near real time and continuous replication between source and target databases. See instructions
- Orchestrate an AI pipeline with data integration
Create an end-to-end pipeline that prepares data and trains a model. See instructions
Data governance
Share, enrich, and govern data.
Scenario: Golden Bank needs to create a business vocabulary to describe and manage data assets, and then make those assets available in a self-service catalog.
Click a tutorial for this use case to get started:
- Curate high quality data
Create high quality data assets by enriching your data and running data quality analysis. See instructions
- Protect your data
Control access to data in a catalog. See instructions
- Consume your data
Evaluate, share, shape, and analyze data. See instructions
- Govern virtualized data
Enrich virtualized data and ensure that virtual data is protected. See instructions
- Configure a 360-degree view
Set up, map, and model your data to create a 360-degree view of your customers. See instructions
Learn more
Parent topic: Getting started