Data Product Consumer Learning Track¶
This track prepares you to harness the full potential of Data Products, enabling faster insights and streamlined integration into your applications and tools.
By the end of this course, you will be able to:
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Efficiently discover Data Products: Learn to browse and search curated Data Products by domain, use case, or specific criteria, ensuring you find the exact data you need for your analysis.
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Ensure confidence in Data Products: Implement solutions that access trusted Data Products in a standardized manner, reducing development time.
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Activate Data Products seamlessly: Gain hands-on experience in connecting Data Products to BI tools, AI platforms, or even syncing live with Excel, enabling you to access and utilize insights quickly.
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Utilize self-serve APIs: Use API and GraphQL capabilities to build custom applications and deploy solutions without technical barriers, speeding up your time to insights.
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Understand business-ready data: Leverage the semantic layer to interpret technical data in business-friendly terms, ensuring consistent data definitions across your organization.
Module 1: Understanding Data Products¶
Get a solid foundation on what Data Products are and how they can drive insights and decision-making. Learn about their features, and importance in business processes.
Module 2: Discovering Data Products¶
Learn how to navigate the Data Product Hub (DPH) to find Data Products that meet your needs using search, filters, tags, and categories.
Module 3: Viewing Data Product info¶
Access key details of the data product—contributors, tier, type, and tags and more, along with links to relevant Git repository and Jira for easy reference and collaboration to make informed decisions on data product usage.
Module 4: Exploring Input and Output data¶
Explore the input and output datasets that are either fed into or generated by the data product for consumption. Use Metis to access detailed metadata and Workbench for advanced data exploration and querying.
Module 5: Navigating semantic models¶
Explore semantic models to understand relationships between data entities. Visualize how data flows from input datasets to create meaningful metrics. Understand the data flow, relationships, and transformations that drive insights.
Module 6: Checking data quality¶
Learn how to assess data quality through key factors like accuracy, consistency, and timeliness to ensure reliable analysis. View the quality checks applied to ensure the Data Product meets data standards.
Module 7: Integrating Data Products with BI tools and applications¶
Unlock the power of Data Products by connecting them to BI tools. Learn to use the data product in Jupyter Notebooks for AI/ML development, query data via Postgres or GraphQL, and easily integrate with your apps using flexible APIs.