Data Product Consumer Track¶
Overview
Welcome to the Data Product Consumer Track—your starting point to explore, analyze, and activate Data Products with confidence. This track shows you how to discover the right data, explore it visually, and use it to power decisions—all within DataOS.
Who is this track for?¶
| Persona | Why It Matters | Level |
|---|---|---|
| Data Analysts / Business Analysts | Discover and activate Data Products to drive intelligent outcomes in daily workflows. | Must-have |
| Data Scientists | Leverage Data Products to extract insights using advanced analytics and machine learning techniques. | Must-have |
| App Developers | Use Data Products to build applications that deliver personalized experiences and business value. | Must-have |
| Product Managers | Bridge the gap between technical teams and business needs to ensure Data Products deliver impactful outcomes. | Must-have |
| AI Product Managers | Align product strategy with Data Products and leverage LLMs and NLP interfaces for AI-driven insight generation. | Must-have |
| Technical Leads / Architects | Improve solution design and team collaboration by understanding how Data Products are consumed and activated. | Recommended |
| Data Product Owners | Gain visibility into how Data Products are being used and ensure they align with end-user needs and business goals. | Recommended |
Basic data utilization is about using data through dashboards, simple queries, and self-service tools to support daily decisions. It focuses on consuming curated data products with no need for coding.
Advanced data utilization involves building models, performing complex analyses, and creating derived datasets using APIs, scripting, or modeling tools. It requires deeper technical expertise to generate new insights or automate decisions.
📚 Core modules¶
This track introduces the concepts, capabilities, and practical workflows that enable Data Product consumers to confidently explore and extract value from data within DataOS.
In this learning track, you will get a comprehensive introduction to Data Products, covering their types and importance in driving insights. You'll learn to navigate the Data Product Hub (DPH), access essential data product information, analyze input/output for meaningful insights, explore semantic models, assess data quality, and understand governance policies for data security.
📚 Module overview¶
| No. | Module | Description | Key Topics |
|---|---|---|---|
| 1 | Understanding Data Products | What are Data Products? Why are they important? Learn about their features and impact on insights. | Features, value in business context |
| 2 | Discovering Data Products | Navigate the Data Product Hub to find the right Data Product for your use case. | Search, filters, categories, metrics, perspectives |
| 3 | Viewing Data Product Info | Access metadata, ownership, tags, and Jira/Git links to assess suitability and collaborate. | Contributors, tier, linked repositories |
| 4 | Exploring Input and Output Data | Understand what data feeds into and comes out of each Data Product. | Input/output schemas, metadata via Metis, queries via Workbench |
| 5 | Navigating Semantic Models | Explore relationships between business entities and metrics within a semantic model. | Visual exploration of metrics, dimensions, and lineage |
| 6 | Checking Data Quality | Review quality checks that ensure reliability of the Data Product. | Accuracy, consistency, timeliness, profiling |
| 7 | Managing Data Governance | Learn how access policies and compliance are enforced within Data Products. | Role-based access, governance guardrails |
| 8 | Integrating Data Products with Tools & APIs | Activate Data Products in BI tools, notebooks, and apps through endpoints and APIs. | Lens, Talos, Postgres, GraphQL, REST, Jupyter integration |
✅ Start learning¶
Ready to Dive In?
