Time Series Database and Its Use Cases

Today’s digital space is crowded with various devices like smart homes, autonomous crypto currency /stock trading algorithms, energy sensor systems or sales forecasting applications among others.  All these applications share a common point – they are all based on a type of data measuring how things change over time. Welcome to time-series data. Classic examples of time-series data are data on temperature, air pressure or car velocity data. In this article you will gain some insights on the time-series database and its various real-world use cases. 

What is a time-series database? 

A time-series database (TSDB) is a database optimized for storing and using time-stamped or time-series data. If you look closely, however, you'll notice that each of these apps requires a different type of data: 

• Self-driving automobiles collect data on how their immediate surroundings change over time. 

• Automated homes keep track of what's happening inside to maintain a comfortable temperature, detect intruders and respond to our commands ("Alexa, play some soothing music"). 

• Retail business watches how its assets move with such precision and efficiency that many of us take cheap same-day delivery for granted. 

These systems use data that tracks how things change over time. The primary axis here is time. Time-stamped values, events, and measurements are held and managed in a Time Series Database.  

Time-series databases include common functions of time-series data analysis, which is helpful when working with time-series data. Time-series data grows at a fast pace with a plethora of data sources.  

With the massive growth of data today, the volume of time-series data produced daily is vast. Moving data across data sources becomes a challenge. Time-series databases offer optimization to scale well, with time being a common denominator and perform much better than other databases. 

How does a time series database work? 

A time-series database stores data in a combination pair of 'time' and 'value.' It makes it easy to analyze the data or a sequence of points recorded in order over time. 

Here is a primary illustration of how the time-series database work. Imagine you have sensors at different places on a farm field. These sensors periodically send data, creating a series of collected measurements over time. This data arrived is almost always recorded as a new entry as the information typically comes in time order.  

What makes time-series data so powerful is the practice of capturing every change to the system as a new, separate row. It enables us to track and anticipate change by analyzing how something changed in the past, monitoring how it is changing now, and predicting how it will change using various data models. 

Time-series databases imply how one can store a set of fixed and dynamic values. A storage medium keeps these records to allow for quick reads and writes dependent on time. 

For example, multiple rig metrics readings take place in an oil well. One set of data points can be labeled "Oil Pressure Rig -1," with the associated dynamic values being the pressure measurement and the date. To track trends in oil pressure, one can use a sample time-series data which, when combined with other variables, can lead to forecasts about maintenance needs and decisions about well abandonment. 

The order of the data points is a natural property of the data because each item has a separate timestamp. One may utilize this data to supply data to a stream processing engine by treating the ordered data as a data stream. Because one of the critical goals of a time-series database is to be fast, a fast stream processing engine is usually the best option. 

Use cases of time series database: 

Time-series databases are increasing in popularity due to resource-efficient data storage, lightning-fast data queries, accurate and meaningful time series measurement and ability to easily measure how datasets change over time providing trends, patterns and insights. Here are some interesting use cases.  

• Scrutinizing web services and analyzing infrastructure:  

• You can utilize time series databases to monitor your applications and websites. For example, Prometheus is a time series database that allows engineers to track performance patterns over time. It helps the team to spot problems, plan maintenance activities, and respond to issues promptly to maintain the best user experience.  

• Accessing IoT data 

• A single connected car generates around 25 GB of data per hour, so you can imagine how much data all IoT devices generate. Mobile devices, eCommerce applications, cars, and inventory management systems are all-time-stamp data. Quick time-series data intake is essential for IoT devices and metrics to collect and store data continuously.  

For example, determining whether or not water is becoming excessively acidic can be done by monitoring the pH level of the water in the vat. Data from IoT endpoints necessitates highly scalable time-series databases.  

• Anomaly Detection: 

Anomaly detection helps find outliers in time series data. Time-series data capture value changes in the system. Organizations can utilize these variables to track current occurrences, analyze historical data, and forecast future events. Anomaly detection relies on virtualization, which can help you correlate outliers. A time series plot, for example, helps visualize outliers. Automated anomaly detection speeds up the process and provides real-time insights. 

• Analyzing and predicting shopping behavior:  

Retailers must constantly forecast future sales to stock their shops properly. Many e-commerce systems save all of an item's information, including product inventory, logistical data, applicable environmental data, transaction amounts, all products purchased from the shopping cart, payment data, and order information. 

In this situation, a TSDB will collect and analyze massive volumes of data fast to identify, for example, what to recommend to customers for their next purchase, optimize inventory, or forecast future buying behavior.  

Retailers can employ statistical models with past data and cross-reference it with customer behavior trends to anticipate future patterns and make informed decisions about which products to keep in stock and when thanks to time series databases. 

Conclusion: 

As you can see, time-series databases have an increasing number of applications. But, when it comes to fulfilling their full potential, this is only a beginning. In addition, every possible surface in the world is getting instrumentally digitized– streets, automobiles, industries, power grids, ice caps, satellites, clothes, phones, microwaves, milk containers, planets, and human beings.  

We are increasingly surrounded by sensors wherever we go. As a result, most goods within and outside us now emits a constant stream of measurements, events, and time-series data. The future will see more innovative use cases for time-series databases.