I started worrying at one point that all the techniques I learned when I started my career for working with big data were becoming obsolete, but they aren't. What you needed to do before to make things possible is now needed to make it fast.
New issues appear when you have to analyze 2Tb with a 32gb RAM machine, but when the order of difference is the same, the issues and thus the answers are the same as before?
Also, the rest of the use cases (which fits into a single machine memory now), can be handled much more efficiently with memory base algorithm, instead of I/O based algorithms.
The goal of Hadoop, as well as most of the theory on disk-based indices (E.g. BTREE), was to overcome the I/O bottlenecks. But as memory is getting bigger and cheaper there is a trend to drop Hadoop in favor of reading data directly from the cloud and into memory.