What Is Time-Series Data?

Time series data is a unique data set that is designed specifically to store time indexed data points. In other words, it’s meant to track data points and key parameters in a timed order. Developers can code in the time specificity as per their project requirements, and chart the data to map greater trends and analysis.

Time series data is used across the board in driverless cars and in financial mapping. In fact, a single connected vehicle will collect over 4K GB of data on a daily basis. This is essential to map, as there could be complex process involved that need to be monitored every second.

The performance of a certain component in relationship to a time metric is also key to understanding deeper patterns that emerge. While data mapping is continuous, it’s important to map the outlined data on a specific time-related axis. 

The need for time-series data

Time series data management goes beyond standard time stamping or second encoding, as some of the largest projects in the world have demonstrated. When new entries are registered as unique, they need to be handled as a single unit. Each sensor can provide innumerable data points that would then need to be compared with other sensors.

Time series data systems treat changes as inserts and not updates to the system. This adds a nuanced element to the tracking process, as each change is recorded and kept as an entry. Data scientists can analyze an event that has occurred in the past, just as easily as something that happens in real time. That’s the power and flexibility of time series data management systems.

Use cases for time series database systems (TSDBs)

TSDBs are one of the fastest growing sectors within the data analytics sector at large. It’s allowing data scientists to collect large quantities of information without losing fidelity or running out of storage. Some of the use cases of TSDBs include software monitoring tools, especially those based on the client site which can be monitored through remote tracking. Physical asset tracking uses sensors to relay information back to the mainframe, while allowing real-time tracking of fleet, packages and documentation. Additionally, a large number of financial traders are using time series database management tools to track the performance of a stock, cryptocurrency, or commodity. Customers can also track their own data with regards to run times, calories burned, or steps taken.

Time series data systems track multiple events in the case where elevation and temperature are simultaneously recorded.

Macro-trends v/s Micro-trends

Companies can track both macro and micro trends depending on the time series data that they capture. Time series DBMS systems have emerged to provide greater clarity when the quantity of data is beyond manual comprehension.

Depending on the time series being analyzed, companies can get a high-level overview as well as a day-to-day segmentation of data. Whether that’s customer data, supply-chain tracking information, or sensor information, time series monitoring tools offer companies multivariate tracking options.

Preservation of key information

The essential goal of a time series data system is to preserve the inherent meaning behind each event. Companies can track log-in information over time to monitor a historical pattern while assigning sessions quality data to each instance. This level of sophisticated requires complete preservation of key data points, which is something that only time series data systems can provide.

Preservation of data also involves storing massive quantities of inputs from all devices. That’s why time series DBM systems have specific queries and resolution systems that simplify the process. From a usability standpoint, TSDBs make the job much easier for data analysts working on the project.