I learnt to write computer code circa 1998, at a time when resources were plenty. I had a computer of my own – an assembled desktop with a 386 processor and RAM that was measured in MBs. It wasn’t particularly powerful, but it was more than adequate to handle the programs I was trying to write.
I wasn’t trying to process large amounts of data. Even when the algorithms were complex, they weren’t that complex. Most code ran in a matter of minutes, which meant that I didn’t need to bother about getting the code right the first time round – apart from for examination purposes. I could iterate and slowly get things right.
This was markedly different from how people programmed back in the 1970s, when computing resource was scarce and people had to mostly write code on paper. Time had to be booked at computer terminals, when the code would be copied onto the computers, and then run. The amount of time it took for the code to run meant that you had to get it right the first time round. Any mistake meant standing in line at the terminal again, and further time to run the code.
The problem was particularly dire in the USSR, where the planned economy meant that the shortages of computer resources were shorter. This has been cited as a reason as to why Russian programmers who migrated to the US were prized – they had practice in writing code that worked for the first time.
Anyway, the point of this post is that coding became progressively easier through the second half of the 20th century, when Moore’s Law was in operation, and computers became faster, smaller and significantly more abundant.
This process continues – computers continue to become better and more abundant – smartphones are nothing but computers. On the other side, however, as storage has gotten cheap and data capture has gotten easier, data sources are significantly larger now than they were a decade or two back.
So if you are trying to write code that uses a large amount of data, it means that each run can take a significant amount of time. When the data size reaches big data proportions (when it all can’t be processed on a single computer), the problem is more complex.
And in that sense, every time you want to run a piece of code, however simple it is, execution takes a long time. This has made bugs much more expensive again – the amount of time programs take to run means that you lose a lot of time in debugging and rewriting your code.
It’s like being in the 1970s all over again!
Why would anyone run their Dev code on the full data set?
Do the development and testing on smaller samples, and only the production code runs on the full, big-data set.
Gives the agility of modern programmings/management techniques without the delay of running everything over a massive data set.
WTF did I just read? Looks like you have very little/no experience working with “large amount of data”