Cloud Fundamentals Course
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Data Warehouse

Definition

Massive, specialized databases designed specifically for complex data analytics, reporting, and Business Intelligence (BI). They aggregate petabytes of historical data from multiple sources.

Explain Like I'm New

A standard database is optimized for 'Write fast' (a user buying a shirt). A Data Warehouse is optimized for 'Read massively' (the CEO running a 5-year trend analysis on every shirt ever sold).

Real World Example

AWS Redshift, Azure Synapse Analytics, GCP BigQuery. Every night, a company exports all the sales data from their transactional database and dumps it into BigQuery. Data scientists then write massive SQL queries against BigQuery to generate machine learning models without slowing down the live website.

Common Use Cases

  • •Business Intelligence
  • •Big Data Analytics
  • •OLAP (Online Analytical Processing)

Interview Questions

basic

  • Should you use a Data Warehouse to handle the live login authentication for your website users?

intermediate

  • Why do Data Warehouses use 'Columnar Storage' instead of traditional Row-based storage?

Flash Cards

Question

Use for live logins?

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Answer

Absolutely not. They are terrible at rapid, single-row transactions. They are designed for scanning millions of rows at once for analytics.

Question

Why Columnar Storage?

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Answer

If an analyst writes `SELECT Total_Sales FROM Orders`, a traditional row-database has to read the entire row (Name, Address, Date) just to find the Sales number. A Columnar database stores all the 'Total_Sales' data together on the disk, allowing it to aggregate the sum 100x faster.