you might also have information from web server logs that get ingested into the data warehouse this would allow you to tie together browsing information on the website with what people ultimately ordered for example maybe you could also tie in information from your customer service systemsand measure if there' relationship between browsing behavior and how happy the customers are at the end of the day data warehouse has the challenge of taking data from many different sourcestransforming them into some sort of schema that allows us to query these different data sources simultaneouslyand it lets us make insightsthrough data analysis solarge corporations and organizations have this sort of thing pretty commonly we're going into the concept of big data here you can have giant oracle databasefor examplethat contains all this stuff and maybe it' partitioned in some wayand replicated and it has all sorts of complexity you can just query that through sqlstructured query languageorthrough graphical toolslike tableau which is very popular one these days that' what data analyst doesthey query large datasets using stuff like tableau that' kind of the difference between data analyst and data scientist you might be actually writing code to perform more advanced techniques on data that border on aias opposed to just using tools to extract graphs and relationships out of data warehouse it' very complicated problem at amazonwe had an entire department for data warehousing that took care of this stuff full timeand they never had enough peoplei can tell you thatit' big jobyou knowthere are lot of challenges in doing data warehousing one is data normalizationsoyou have to figure out how do all the fields in these different data sources actually relate to each otherhow do actually make sure that column in one data source is comparable to column from another data source and has the same set of dataat the same scaleusing the same terminologyhow do deal with missing datahow do deal with corrupt data or data from outliersor from robots and things like thatthese are all very big challenges maintaining those data feeds is also very big problem lot can go wrong when you're importing all this information into your data warehouseespecially when you have very large transformation that needs to happen to take the raw datasaved from web logsinto an actual structured database table that can be imported into your data warehouse scaling also can get tricky when you're dealing with monolithic data warehouse eventuallyyour data will get so large that those transformations themselves start to become problem this starts to get into the whole topic of elt versus etl thing