the rise of graph databases data the following query demonstrates how to delete all nodes and relationships in the databasematch(noptional match ( )-[ ]-(delete , now that we're acquainted with connected data and have basic knowledge of how it' managed in graph databasewe can go step further and look into reallive applications of connected data social graphfor examplecan be used to find clusters of tightly connected nodes inside the graph communities people in cluster who don' know each other can then be introduced to each other the concept of searching for tightly connected nodesnodes that have significant amount of features in commonis widely used concept in the next section we'll use this ideawhere the aim will be to find clusters inside an ingredient network connected data examplea recipe recommendation engine one of the most popular use cases for graph databases is the development of recommender engines recommender engines became widely adopted through their promise to create relevant content living in an era with such abundance in data can be overwhelming to many consumers enterprises saw the clear need to be inventive in how to attract customers through personalized contentthereby using the strengths of recommender engines in our case study we'll recommend recipes based on the dish preferences of users and network of ingredients during data preparation we'll use elasticsearch to quicken the process and allow for more focus on the actual graph database its main purpose here will be to replace the ingredients list of the "dirtydownloaded data with the ingredients from our own "cleanlist if you skipped ahead to this it might be good to at least read appendix on installing elasticsearch so you have it running on your computer you can always download the index we'll use from the manning download page for this and paste it into your local elasticsearch data directory if you don' feel like bothering with the case study you can download the following information from the manning website for this three py code files and their ipynb counterparts data preparation part --will upload the data to elasticsearch (alternatively you can paste the downloadable index in your local elasticsearch data folderdata preparation part --will move the data from elasticsearch to neo exploration recommender system