Time Series Data Storage in MongoDB

Andrew Jackson, Skyline Innovations

June 23 2011

Slides

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.