tl;dr:“After such a dramatic increase in hardware capability, we should ask ourselves, “Do the conditions that drove our scaling challenges in 2003 still exist?” After all, we’ve made our systems far more complex and added a lot of overhead. Is it all still necessary? If you can do the job on a single machine, isn’t that going to be a better alternative?” This post digs into why scale-out became so dominant, take a look at whether those rationales still hold, and then explore some advantages of such architecture.
tl;dr:This post will make the case that the era of Big Data is over. It had a good run, but now we can stop worrying about data size and focus on how we’re going to use it to make better decisions. I’ll show a number of graphs; these are all hand-drawn based on memory. If I did have access to the exact numbers, I wouldn’t be able to share them. But the important part is the shape, rather than the exact values.