Statistical sampling have established itself in all facets of our live from physics to medical research to presidential elections, still when it comes to Big Data we most frequently favor brute force approach and attempt to process our entire data set - it's all or nothing. However we don't really need to count every single grain of sand at the beach to conclude that it will be a great holiday destination. When we analyze our business performance do we compare every digit of last week 365,514,134 visitors to this week?s 366,364,615 or do we want to know one is 0.2% bigger than the other? Or maybe we can say there is no difference? Properly posing questions to Big Data is the key to reducing overall costs of the data systems and getting information faster while preserving brute force crunching for tasks that really have to count every penny and every drop in the ocean. We will present sampling methodologies useful for Hadoop environments, properly structuring the data for export to non-Hadoop systems, discuss establishing proper sampling rate for different tasks, emphasizing its application to digital marketing and variable sampling rate for properly tracking valuable needles in unimportant haystacks.