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Big data technologies solve problems that cannot be implemented with SQL-based solutions or are extremely difficult and expensive. However, since these new technologies are largely based on a fundamentally different approach, they also bring with them new challenges. You should therefore consider which technology or combination of technologies you use to solve these challenges. And not, as often happens in practice, start a migration or expansion without a long-term strategy, as this often comes back to haunt you with high costs. It is important to consider all phases of the process right from the start - from data acquisition to transformation to analysis and beyond. You should also not lose sight of the operational benefits. Using technology just for the sake of technology can be fun, but often doesn't get beyond the status of a "Jugend forscht" project.

Mistake 3: Believing that traditional relational database know-how is sufficient for Hadoop
There is still a belief that benin telegram screening a relational database management system (RDBMS) can be replaced 1:1 with Hadoop, thus obtaining a more powerful system at a lower cost. However, since Hadoop is not an RDBMS, but a framework on a distributed file system, this approach does not work.

The key here is to select the right technologies and, above all, overall architectures and to plan them thoroughly. SQL-on-Hadoop is making significant progress, but in many respects it is still a long way from outperforming a classic, mature RDBMS. Many of the new SQL-on-Hadoop technologies already offer a lot, especially for analytical use. However, in order to use them correctly, you nee.