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Data Product Foundations Track: Course II

Overview

Welcome to the second course of the Data Product Foundations Track! In this hands-on journey, you'll build a consumer-aligned data product that transforms raw data into structured, business-ready insights for reporting, dashboards, and self-service analytics.

🌟 What you’ll learn

By the end of this course, you’ll be able to:

  • Define business-centric Data Products
    Capture use case requirements, KPIs, and metrics that guide the design of meaningful data products.

  • Model, transform, and secure data
    Build semantic models, apply business logic, and implement access control to ensure secure, governed consumption.

  • Enable consumption across tools
    Expose outputs via semantic models and APIs for seamless use in BI tools, notebooks, and apps.

  • Package and deploy your Data Product
    Bundle your resources, define the spec, and register your product in Data Product Hub and Metis.

📘 Scenario

You’re continuing work on the Retail Data Product, and now it’s time to make it usable for business consumers. Your data team has cleaned and organized raw inputs—but now the focus shifts to creating semantic layers, applying business logic, and enabling access through secure endpoints and tools like Lens and GraphQL.

Your goal? Build a consumer-aligned data product that transforms structured data into insights. You’ll define business rules, model key metrics, and expose them in the right formats for business teams.

📚 Learning modules

Module 1: Define Business Requirements

To create a successful Data Product, it’s crucial to first understand the business context and requirements. This module focuses on capturing key needs and expectations for Data Products for your organization based on Righ to Left approach:

Module 2: Design Data Product

Module 3: Create a Repo for Versioning

Module 4: Connect with Source Systems

Module 5: Transform & Ingest

Module 6: Create Semantic Model

Module 7: Deploy and Register Data Product

How to use these modules

Each module in this track is designed for self-paced, hands-on learning.

To follow along:

  1. Open your preferred code editor and create a new file with a .yaml extension.

  2. Based on your objective (e.g., creating a data pipeline, configuring access policies), copy the relevant YAML snippets provided in the training materials.

  3. Modify the snippets as needed to suit your use case—update names, paths, and credentials as appropriate.

  4. Login to your DataOS training instance via the CLI.

  5. Use the dataos-ctl apply command to deploy and test your changes.

Each section includes specific instructions and configuration details to guide you through the process.

Checklist for success

Make sure you complete the following:

  • ✅ Objectives and KPIs documented
  • ✅ Semantic model mapped with dimensions and measures
  • ✅ Repo structure and versioning in place
  • ✅ Ingest and transform logic verified
  • ✅ Output data quality checked and profiled
  • ✅ Lens, manifests, and access controls configured
  • ✅ Data Product deployed and registered in DPH

You’re now ready to create a business-facing, consumer-aligned Data Product. Let’s get started!