data science in big data world graph databases--not every problem is best stored in table particular problems are more naturally translated into graph theory and stored in graph databases classic example of this is social network scheduling tools scheduling tools help you automate repetitive tasks and trigger jobs based on events such as adding new file to folder these are similar to tools such as cron on linux but are specifically developed for big data you can use themfor instanceto start mapreduce task whenever new dataset is available in directory benchmarking tools this class of tools was developed to optimize your big data installation by providing standardized profiling suites profiling suite is taken from representative set of big data jobs benchmarking and optimizing the big data infrastructure and configuration aren' often jobs for data scientists themselves but for professional specialized in setting up it infrastructurethus they aren' covered in this book using an optimized infrastructure can make big cost difference for exampleif you can gain on cluster of serversyou save the cost of servers system deployment setting up big data infrastructure isn' an easy task and assisting engineers in deploying new applications into the big data cluster is where system deployment tools shine they largely automate the installation and configuration of big data components this isn' core task of data scientist service programming suppose that you've made world-class soccer prediction application on hadoopand you want to allow others to use the predictions made by your application howeveryou have no idea of the architecture or technology of everyone keen on using your predictions service tools excel here by exposing big data applications to other applications as service data scientists sometimes need to expose their models through services the best-known example is the rest servicerest stands for representational state transfer it' often used to feed websites with data security do you want everybody to have access to all of your datayou probably need to have fine-grained control over the access to data but don' want to manage this on an application-by-application basis big data security tools allow you to have central and fine-grained control over access to the data big data security has become topic in its own rightand data scientists are usually only confronted with it as data consumersseldom will they implement the security themselves in this book we don' describe how to set up security on big data because this is job for the security expert