handling large data on single computer be processed when we refer to large data in this we mean data that causes problems to work with in terms of memory or speed but can still be handled by single computer we start this with an overview of the problems you face when handling large data sets then we offer three types of solutions to overcome these problemsadapt your algorithmschoose the right data structuresand pick the right tools data scientists aren' the only ones who have to deal with large data volumesso you can apply general best practices to tackle the large data problem finallywe apply this knowledge to two case studies the first case shows you how to detect malicious urlsand the second case demonstrates how to build recommender engine inside database the problems you face when handling large data large volume of data poses new challengessuch as overloaded memory and algorithms that never stop running it forces you to adapt and expand your repertoire of techniques but even when you can perform your analysisyou should take care of issues such as / (input/outputand cpu starvationbecause these can cause speed issues figure shows mind map that will gradually unfold as we go through the stepsproblemssolutionsand tips not enough memory processes that never end problems some components form bottleneck while others remain idle not enough speed solutions handling large data general tips figure overview of problems encountered when working with more data than can fit in memory computer only has limited amount of ram when you try to squeeze more data into this memory than actually fitsthe os will start swapping out memory blocks to diskswhich is far less efficient than having it all in memory but only few algorithms are designed to handle large data setsmost of them load the whole data set into memory at oncewhich causes the out-of-memory error other algorithms need to hold multiple copies of the data in memory or store intermediate results all of these aggravate the problem