Now that you understand dimensional modeling, let's explore how to physically store that data for maximum analytical performance.
The file format you choose can make queries 10x faster or slower! It's not just about storage space - it's about how efficiently you can read the data.
Engagement Message
Recall a time one data file loaded much faster than another—what formats were involved?
Traditional databases store data in rows - perfect for transactional systems where you need complete records. But analytics usually focuses on specific columns across millions of rows.
Reading row-by-row to analyze one column is like reading every page of a book to find specific words!
Engagement Message
What would be a more efficient way to find specific words in a book?
Column-oriented storage formats like Parquet and ORC store data by columns instead of rows. This makes analytical queries dramatically faster.
When you want to analyze sales amounts, you read only the sales_amount column instead of entire customer records.
Engagement Message
Why would reading just one column be faster than reading entire rows?
Parquet has become the gold standard for analytics. It groups similar values together, making compression incredibly effective and queries lightning-fast.
A column of mostly "New York" values compresses to a tiny fraction of its original size.
Engagement Message
How might grouping similar values help with compression?
Here's the magic: Parquet can skip entire chunks of data during queries. If you're looking for sales from January, it can ignore all the chunks containing February data.
This "predicate pushdown" eliminates scanning irrelevant data, making queries finish in seconds instead of minutes.
Engagement Message
What's the benefit of skipping irrelevant data during queries?
