etl versus elt let' first talk about etl what does that stand forit stands for extracttransformand load and that' sort of the conventional way of doing data warehousing basicallyfirst you extract the data that you want from the operational systems that you want sofor examplei might extract all of the web logs from our web servers each day then need to transform all that information into an actual structured database table that can import into my data warehouse this transformation stage might go through every line of those web server logstransform that into an actual tablewhere ' plucking out from each web log line things like session idwhat page they looked atwhat time it waswhat the referrer was and things like thatand can organize that into tabular structure that can then load into the data warehouse itselfas an actual table in database soas data becomes larger and largerthat transformation step can become real problem think about how much processing work is required to go through all of the web logs on googleor amazonor any large websiteand transform that into something database can ingest that itself becomes scalability challenge and something that can introduce stability problems through the entire data warehouse pipeline that' where the concept of elt comes inand it kind of flips everything on its head it says"wellwhat if we don' use huge oracle instancewhat if instead we use some of these newer techniques that allow us to have more distributed database over hadoop cluster that lets us take the power of these distributed databases like hiveor sparkor mapreduceand use that to actually do the transformation after it' been loadedthe idea here is we're going to extract the information we want as we did beforesay from set of web server logs but thenwe're going to load that straight in to our data repositoryand we're going to use the power of the repository itself to actually do the transformation in place sothe idea here isinstead of doing an offline process to transform my web logsas an exampleinto structured formati' just going to suck those in as raw text files and go through them one line at timeusing the power of something like hadoopto actually transform those into more structured format that can then query across my entire data warehouse solution