The big data hype of the 2010s has almost been forgotten in the wake of new trends such as artificial intelligence. In addition to numerous scalable NoSQL databases and frameworks that make it quite convenient to store and process large amounts of data in modern cloud systems, there are also a number of scalable algorithms and data structures that largely secretly contribute to making data-intensive systems more performant.
This article, in turn, is intended to help draw more attention to these approaches and the exciting ideas behind them. After all, their use allows data to be processed more efficiently and thus ultimately in a more resource- and environmentally-friendly way. This albania telegram screening applies not only to large but also to smaller amounts of data.
To put it bluntly: Big data occurs when the data no longer fits on one box, but has to be distributed across multiple machines. This may be due to the sheer volume of data ( volume ), the required processing speed ( velocity ) or different data or processing types ( variety ). This "scaling out" to many servers, which is common today, is intended to keep data-intensive systems as infinitely scalable as possible, so that a growth in the amount of data can always be accommodated with a correspondingly higher number of servers.
In addition to the well-known problems of distributed systems, such as synchronization, redundancy and resilience, the complexity of the processing running on them also plays a role that should not be underestimated. The scalability just mentioned only works if the processing complexity is at most linear (i.e. in O(n) ). Simply put, twice the amount of data can then be processed with twice the hardware resources.