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Azure for DE: 29 - Introduction to dimensional modeling
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Data Engineering on Microsoft Azure - Azure for DE: 29 - Introduction to dimensional modeling

Master Azure Data Engineering: From Ingestion to Analytics with Real-World Projects

4.0 (5)
45 learners

What you'll learn

Design and implement a data ingestion pipeline using Azure Data Factory
Apply security and access controls to an Azure Data Lake Storage account
Transform raw data into a modeled structure using Azure Databricks or Synapse Spark
Deploy and manage Azure Data Factory pipelines using CI/CD practices

This course includes

  • 49.5 hours of video
  • Certificate of completion
  • Access on mobile and TV

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Full Transcript

Greetings, data engineers! Achievement unlocked - we've successfully loaded data into the raw layer of our data lake. What's our next step? Should we construct reports directly from this data? If not, what are the reasons behind this decision? Join me for the 29th episode of my free DP-203 course, where I'll address these queries and delve into the fundamentals of dimensional modeling. Enjoy! ▬▬▬▬▬▬ IMPORTANT LINKS ▬▬▬▬▬▬ My LinkedIn profile: https://www.linkedin.com/in/piotr-tybulewicz-81a8793/ GitHub with my drawings: https://github.com/TybulOnAzure/DP-203 Why star schema is your best choice in Power BI: https://www.youtube.com/watch?v=KBqRC6JWy9A ▬▬▬▬▬▬ MEMBERSHIP ▬▬▬▬▬▬ Join this channel to get access to perks: https://www.youtube.com/channel/UCLnXq-Fr-6rAsCitq9nYiGg/join ▬▬▬▬▬▬ CHAPTERS ▬▬▬▬▬▬ 00:00 Introduction 00:22 Common BI flow revisited 04:00 Data issues 18:50 Where does it fit? 24:17 Facts and dimensions 36:43 2nd example 43:36 Snowflake 51:33 Facts vs dimensions 54:37 Summary

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