This article provides a comprehensive engineering practice for real-time comparative analysis using DolphinDB, a time-series database. It covers three key metrics: year-over-year (YoY), month-over-month (MoM), and baseline deviation calculations. The author demonstrates how to build a robust pipeline that handles data ingestion, computation, and visualization in real-time. This is particularly valuable for developers working with financial data, operational metrics, or any time-series analytics where timely comparisons are critical. The engineering patterns discussed—such as incremental computation and efficient window functions—are applicable beyond DolphinDB to other time-series databases like InfluxDB or TimescaleDB. For overseas developers, this offers insights into how Chinese engineers approach real-time analytics at scale, with practical tips on performance optimization and error handling.
A practical guide to implementing real-time comparative analytics with DolphinDB, covering key metrics and engineering patterns.