Part 2: Intro to Data Warehousing
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We'll zoom out and take a high-level look at data warehousing. These concepts will be demonstrated through a fictional scenario: how Google could collect, store, and model its search data.
You'll learn:
- Data collection: Understand how online searches generate massive amounts of data, from search queries to metadata about users, sessions, and devices, and how this data is captured by systems like Google Search Console and stored in BigQuery.
- Data modeling: Dive into data modeling concepts, including the basics of Star Schemas, which optimize data storage and retrieval for analytical purposes.
- ETL vs. ELT: Explore the differences between traditional ETL (Extract, Transform, Load) and modern ELT (Extract, Load, Transform) processes and how cloud data warehouses like BigQuery revolutionize data handling.
- Flattened tables and views: Learn about creating flattened tables and views to simplify analysis, making accessing and aggregating data without complex joins easier.
- Data governance and OLAP cubes: Discover the importance of data governance and the role of OLAP cubes in multidimensional analysis, allowing for filtering and aggregation across multiple dimensions.
- Practical applications: Apply these concepts in real-world scenarios, whether in e-commerce, SaaS, or other industries and learn how to set up efficient data pipelines for better business insights.
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