Time-Series Database Use Cases

Accessing IoT Data

Most IoT implementations, such as connected water, energy, and temperature meters, necessitate regular data collecting and reporting. Seasonal patterns, average consumption, and inefficiencies can all be identified via time-series analysis, which provides time-stamped data points. A connected pH meter attached to a TSDB, for example, can alert a technician in charge of maintaining a certain pH level that a specific vat of water is becoming too acidic. Massive volumes of data are collected by IoT endpoints which require highly scalable time-series databases.

Forecasting Financial Trends

Accurately predicting financial trends using just time-series data is extremely difficult. A TSDB, on the other hand, can give a lot of contextual data to aid analysts. Consider the stock market: a significant surge in airline stock could coincide with holiday travel. Alternatively, a change in corporate leadership may frighten investors, leading the stock to drop briefly. Cross-referencing data is simple with time-series databases which results in a richer, clearer picture.

Monitoring Web Services

Time-series databases can be used by businesses to assess the success of their applications and web properties. The open-source monitoring system Prometheus, for example, is a time-series database that allows engineers to track performance patterns across time. This helps them to quickly notice when problems arise, allowing them to schedule maintenance and respond to occurrences to maintain an optimal user experience. Some web and mobile apps use a TSDB to keep track of certain events such as a button click, playing a video, or sharing some content. They can use these events to map a user's path, highlight challenges or performance bottlenecks, and streamline more sophisticated activities.

Sales Forecasting

Retail shops are obligated to constantly estimate future sales in order to appropriately stock their shelves with merchandise. Thanks to time-series databases, retailers can use statistical models based on historical data and cross-reference the data with customer behavior trends to predict future patterns and make informed decisions about which products to keep in stock and when.

Anomaly Detection

Anomaly detection aids in the detection of out-of-the-ordinary aberrations in time-series data. When a system change occurs, time-series data captures a value. Organizations can use these values to track changes, uncover how changes occurred in the past, keep track of what's going on now, and use the accumulated data to forecast future occurrences.

A major aspect of detecting anomalies is virtualization. A time-series graphic, for example, provides the visual aid that many people want while looking for outliers. Another option is to use automated anomaly detection, which can speed up the process by providing real-time information. This makes it possible to swiftly connect outliers.