So, the other day, I got this task to pull out a list of parent company names from a huge dataset. I thought, “Piece of cake!” Oh boy, was I wrong.
First, I tried the regular route. Opened up the dataset in a spreadsheet program, thinking I could just sort and filter my way through. Big mistake. The file was massive, and my poor laptop just couldn’t handle it. It was like trying to stuff an elephant into a Mini Cooper.
The Hard Way
- Opened the giant dataset (it took forever!).
- Tried to use built-in filters.
- Spreadsheet program froze – total fail.
Okay, Plan B. I needed something more powerful. So, I remembered some basic database stuff I learned years ago. I decided to give that a shot.
I fired up a database tool and started importing the data. This took a good chunk of time, I must say. I went and made myself a cup of coffee, came back, and it was still chugging along.
The “Slightly” Less Hard Way
- Imported that monster file into a database program.
- Waited… and waited… and waited some more.
Once the data was finally in, I started writing a query. Now, I’m no SQL expert, but I know enough to get myself into trouble. My first few attempts were… let’s just say, less than successful. I kept getting either way too much information, or nothing at all. It was like playing a really frustrating game of hide-and-seek with the data.
After a lot of trial and error, and some serious Googling, I finally crafted a query that worked! It felt like discovering the secret recipe for Coca-Cola. Basically, I had to find a way to link the subsidiaries to their parent companies based on a shared ID.
Finally
The final is I finally found the method to find all the parent company name I want.
It was a long and bumpy road, but hey, I got there in the end. And I learned a valuable lesson: always respect the size of your data, and don’t be afraid to ask for help (or at least consult the internet gods).
