Some of the largest datasets have strong time components, like stock market data, server logs, weather data, or even just the temperature in the server room. Despite so many real-world applications of time-series analysis, most storage & retrieval options are either highly proprietary, or worse, relational! MongoDB's dynamic schema nature and map-reduce functionality can replace and improve on traditional business intelligence solutions for data warehousing and make such enterprise-level buzzwords easy. If you've ever had to use a star schema, you might be surprised at how much easier it is in MongoDB! This talk will detail how we at Skyline Innovations, a solar energy startup in DC, has used MongoDB to store and organize our metrology data from our many commercial scale solar projects, and some of the obstacles we've faced as a startup. Includes background on traditional data warehousing techniques, modern approaches using MongoDB, map/reduce, plumbing a laundromat, and an arduino.