Data aggregation and analysis problems become notoriously thorny as traffic scales up: conventional databases break down at scale, and map/reduce frameworks such as Hadoop have a substantial developer and operational complexity burden. Wanelo, an online community for all the world's shopping bringing together stores, products and 10M users all in one social platform, became frustrated that the aggregation and analysis tools used when data was small (venerable Unix data processing utilities like grep, awk, cut, sed, uniq and sort) couldn't be used when data became large. Upon discovering Manta, a new cloud-based object storage system that enables the storing and processing of data simultaneously, Wanelo had a solution that no longer required the need to move data between storage and compute. Building on Manta, Wanelo has developed a system for data analysis that allows the team to tackle big data analysis using Unix utilities, resulting in a cost-effective and scalable solution. In this talk Konstantin discussed Wanelo's experiences building their system on Manta, including their motivations and considered alternatives that led to a Manta-based implementation of fully-parallelized cohort retention analysis in four lines of shell.