Okay, so I’ve been messing around with data cubes lately, trying to get my head around how to make them work better and faster. It’s been a bit of a journey, so I figured I’d share some of the stuff I’ve picked up along the way.
First off, I started by just throwing everything into the cube. You know, every single dimension, every measure, just everything. Big mistake! It was slow as molasses. The queries took forever, and honestly, it was just a mess to navigate.
Figuring out what really matters.
So, I took a step back and really thought about what I needed to see. What questions was I actually trying to answer with this data? Turns out, I didn’t need everything. I identified the key dimensions that were most important for my analysis and focused on those.
- I picked out the core dimensions, like date, product category, and customer region.
- I ditched the stuff that was too granular, like individual transaction IDs, at least for this particular cube.
Getting smart about Aggregations
Next up, I realized I was doing a lot of calculations on the fly. Every time I ran a query, the system was crunching numbers from scratch. No wonder it was slow!
So, I started looking into pre-aggregated data. Basically, I figured out what calculations I was doing most often (like total sales per month, average order value, etc.) and created pre-calculated versions of those. It’s like having the answers ready before anyone even asks the question.
- I made monthly summaries for sales.
- I set up average value calculations for product categories.
Optimize, optimize, optimize
After pre-calculations, I Started to think about Optimization. This part can be a lot detailed, but don’t worry.
- I Made several simple test cases for different data set.
- I Tried different way for same calculations.
Finally, after I did all these, The cube became more useful and faster than ever before. And now I can get my data much easier!
