remember when we talked about etl and elt earlier in the bookthis is good example of where you might actually be loading raw data into system and doing the transform on the system itself that you used to query your data you can take raw text files that haven' been processed at all and use the power of spark to actually transform those into more structured data it can also talk to things like hiveso if you have an existing hive database set up at your companyyou can create hive context that' based on your spark context how cool is thattake look at this example codeijwf$uy )jwf$poufyu td spxt ijwf$uytrm  &-&$ obnfbhf' vtfstyou can actually create an rddin this case called rowsthat' generated by actually executing sql query on your hive database more ways to create rdds there are more ways to create rdds as well you can create them from jdbc connection basically any database that supports jdbc can also talk to spark and have rdds created from it cassandrahbaseelasticsearchalso files in json formatcsv formatsequence files object filesand bunch of other compressed files like orc can be used to create rdds don' want to get into the details of all thoseyou can get book and look those up if you need tobut the point is that it' very easy to create an rdd from datawherever it might bewhether it' on local filesystem or distributed data store againrdd is just way of loading and maintaining very large amounts of data and keeping track of it all at once butconceptually within your scriptan rdd is just an object that contains bunch of data you don' have to think about the scalebecause spark does that for you rdd operations nowthere are two different types of classes of things you can do on rdds once you have themyou can do transformationsand you can do actions