At Improve Digital (http://www.improvedigital.com) we collect and process large amounts of machine generated and behavioral data. Our systems address a variety of use cases that involve both batch and streaming technologies. One common denominator of the overall architecture is the need to share models and workflows across both worlds. Another one is that the analysis of large amounts of data often requires trade-offs; for instance trading accuracy for timeliness in streaming applications. One approach to satisfy these constraints is to make "big data" small. In this talk we will review a number of approximation methods for sketching, summarization and clustering and discuss how they are starting to change the way we think about certain types of analytics, and how they are being integrated into our data pipelines.