A very simple (or simplified) example could be - say you have just one table - a transaction table from a local deli shop with a volume of around 500 transactio

A very simple (or simplified) example could be - say you have just one table - a transaction table from a local deli shop with a volume of around 500 transactions / day, and all you are doing with it is producing a report of how many items of each type (sugar/salt/bread) is sold. Say, you produced this report on daily, monthly and yearly basis. You decided to use, say, just a spreadsheet, which is sufficient to store this data, as well as running the calculation using pivot tables. This works for a year. The shop is now more popular, and a bit bigger too. The volume increases to say 2000 transactions a day, and you say, well, let's use mysql to store this. You are still comfortably generating the report at the end of the year using simple sql queries. Now suddenly, they decide go really big, to expand and open more stores across the city or state, say about 500 stores. They also expand the items in the stores from just few hundreds to few thousands. They project the transactions across all stores to be in the range of 1,000,000, on average containing about 10 items in each. They also want more reports on which products are doing good/bad, what are the buying habits, what's the trend over Thanksgiving, do year-to-year comparisons on various matrix. And the mysql solution no longer works - storing 10,000,000 rows on daily basis and running those sql queries is turning out to be practically impossible. In a year, you now have 3,650,000,000 rows that needs to be joined with 100,000 items. Yes, you can add more space and more resources to the machine, but running all those queries are now taking hours or days instead of seconds. This is the point where Hadoop/Map-Reduce comes to rescue. You now have a cluster of, say, 5 machines, each having, say, 64G or RAM and 1 PB of storage, but still costing just around 25000$. Since you are not familiar with Java or Map - Reduce, and/or don't have time to learn it, you decide to use Hive - an important tool of the Hadoop ecosystem among others - that still let's you access and process the data in the familiar sql query way - but generating map-reduce jobs on your behalf on the nodes. They split the processing across different nodes, bring required outputs together, may run through other map-reduce jobs if required, and ultimately, give you the results that you can use produce those reports - in a reasonable time, of course. This is very simplified but real-life business use case.