Here&#x27;s a (simple) problem I solved with Spark:<p>I had hundreds of gigabytes of JSON logs with many variations in the schema and a lot of noise that had to be cleaned. There were also some joins and filtering that had to be done between each datapoint and an external dataset.<p>The data does not fit in memory, so you would need to write some special-purpose code to parse this data, clean it, do the join, without making your app crash.<p>Spark makes this straightforward (especially with its DataFrame API): you just point to the folder where your files are (or an AWS&#x2F;HDFS&#x2F;... URI) and write a couple of lines to define the chain of operations you want to do and save the result in a file or just display it. Spark will then run these operations in parallel by splitting the data, processing it and then joining it back (simplifying).