Time-series data analysis can be approached in two ways. Traditionally time-series data is aggregated into partitioned historical data bases, and then reported on at scheduled intervals. Commonly, reports delivered today cover data collected yesterday. A modern (and perhaps most relevant to Big Data) approach is to recognize that time-series data just “keeps coming”. And since the timeliest analysis could theoretically deliver the most value, visualizations should update as soon as the data streams in.
Square’s evolving Cube library (it’s still early version 0) enables web developers to easily deliver real-time charting of streaming time-series data on dynamic web pages:
Cube is an open-source system for visualizing time series data, built on MongoDB, Node and D3. If you send Cube timestamped events (with optional structured data), you can easily build realtime visualizations of aggregate metrics for internal dashboards.
I’ve spend a large chunk of my professional life working at IT system management vendors, each of whom spent significant resources to build and deliver proprietary event and time-series data analysis and visualization tools. In the last few years there have been successful open source discrete event monitoring and management tools (threshold, alert, etc) that really disrupted the market of old school proprietary event solutions. Open source time-series solutions like Cube have similar potential to disrupt proprietary time-series analysis markets.
Time-Series Data Stream Mining
Real-time time-series visualization is fundamentally data stream mining, maybe not at Big Data scales but certainly there are some hints about the future for Big Data stream mining in the way Cube is architected. Continue reading