Why performance matters in time-series data
Good data from the past helps us make better decisions in the present. Most of today's data were created within the past ten years, and human data output will only grow exponentially from here on. This sudden pervasiveness of data means that we need new ways to store and process information focusing on efficiency and sustainability. This article describes why speed and performance in a time-series database is the key to staying afloat in a sea of data.
The International Data Corporation predicts that the total collected sum of human data will reach 175 zettabytes by 2025. One zettabyte is a billion terabytes of course, or a trillion gigabytes, depending on which mind-bending measurement you prefer.
While we have no issue storing and collecting this data, the real trick lies in how we process it. Forrester data says as much as 73% of the data within an enterprise goes unused for analytics, a huge missed opportunity to capture and process data effectively. That’s why a number of teams are working on competitive products to make data more useful.
QuestDB is concerned with capturing time-series data in particular, which lets us represent and understand change over time. Time-series data might pertain to changes to the weather, changes in a machine’s performance, or even changes in your own weight. But quite unlike weighing yourself once a day and storing those standalone states in a database, time-series data calls for capturing every single tiny fluctuation in your weight, up or down, whenever you sweat, get sick, eat a meal, or use the bathroom.
