Time-series Databases in Real-Time Analytics Overview 

Overview of Time Series Databases

A time-series database is usually put in work to deal with time-stamped data or time-series data. It is also often considered as a series of data that is studied over time. Time-Stamped data would be any data that is collected over some time. For example, this could be any data, an application performance monitoring, stock market trade records, book rental details from an old library, or just a version management database for an application.

Time series data is a set of values organized by time-series data which include sensor data, stock prices, click stream data and application telemetry.

Source: Time Series -Azure

However, a time-series database is built specifically to handle time-stamped events and metrics. Time series database holds history with a finance application, but it has proved to be of significant importance when managing real-time and historical data. The main difference between a time-series database and a normal database is that the queries are always made over time in a time-series database and not on average values. When plotted on a graph, a time-series database would always have time as one axis.

Why is a Time-Series Database Important?

This question comes up as we already have working database models very efficiently. But the present technological market is transforming into a much curious consumer of data. The previously used data for visualizing the results are now used to learn and respond and predict future outcomes. To overcome the chances of loss and reduce the hardware downtime, we need to keep a timely record of how the system responds to the change. This timely record is maintained over a time series data. And working on such data often needs a specialized approach.

This proposed a database that can adapt to the dynamically changing data with dynamic schemas. The database was also required to be elastically scalable and consistent as the data volume grows with time. RDBMS has been serving many of the demands well but was designed for entirely different data kinds. This made use of specialized time series databases popular and acceptable over RDBMS for time series data.

Read more about Anomaly Detection with Time Series Forecasting

From Reactive to Proactive

For a long time now, the time series database has been used as a reactive system to analyze and visualize current trends. This approach focused on working on data from the past and had no contribution to future developments. But as the man’s desire to predict and indulge in the future became more prominent, a significant change in the way the data is processed also came along. The time-series database, which can deliver a proactive response to the time series data, came up to be the best solution.

The new businesses need a proactive approach for their Real-Time predictive actions. With the current business requirement of reacting before a situation becomes a crisis source, reactive business approaches fail badly. The need to adopt time-series databases for a proactive approach is a must for the upcoming business opportunities.

What makes the Time Series Databases Different?

A Time-series database is unique in its way of interacting with the data. Time series data are collected over time with makes the highly voluminous sometimes. A Time-series database specially designed to work with such data has a much better and faster approach to handling such data. However, the storage and retrieval of this data would have been a problem for the relational database.

Many propose using relational databases only instead of these specialized databases, which would seem useless for data other than the time series data. However, this would always need a parent table in the relational database describing the natural keys and a reference key usually smaller to refer back to the natural keys. Whereas an optimized time-series database does the same task, there is no need for reference keys or surrogate keys.

The purpose of cognitive computing is to create the frame for computing such that complex problems are solved easily without human intervention.

Source: Real-Time Anomaly Detection for Cognitive Intelligence

Time Series Databases vs. Relational Databases

  • Time series database makes the storage and computes much cheaper, but they don’t offer solutions that weren’t already there in the market. The relational database has been serving the market well for a long time and holds the full capability to further. But when the data started growing horizontally rather than vertically, handling RDBMS became a whole new pain.
  • Modern time-series databases provide good performance and are scalable.
  • RDBMS provides intelligent business handling solutions
  • Working on historical data with RDBMS has always been an issue as they are hard to scale. For that account, it is common to switch to horizontally scalable systems.
  • Working on data with many entities to consider (vertical data) is troublesome with the time series databases. Even with a high cost and higher storage cost, most companies choose RDBMS in this case.

Both time series and relational databases have their own testified use cases and workforces. Both are capable of performing each kind of computation only in some circumstances one turns out to be useful while in others proves to be beneficial.

Explore more about Different types of Databases and Cloud-Native Databases

Real-time Analytics and Time-Series Databases

Real-time analytics is using the data as it is made available. Most of the real-time analysis is based on the concept of reacting to the data as it came in. The response time is always the main and key axis for the analysis. Time-series databases, however, manage Real-time analysis.

The time-series database came into existence to understand and maintain rapidly increasing internet traffic demands. With the increasing inaccessibility of the internet, there came a huge rise in the number of unstructured data points on the network.

With millions of users came the data of their usage and search demands. This data accumulated with time, and there was no stop to it. It expanded exponentially both horizontally and vertically, so much so that managing over traditional databases became a huge challenge. The time-series database gave the scalability and adaptability to adjust to the users’ highly increasing demands. This is now used for all sorts of time-stamped data and has been behind many tech giants’ success stories for a few decades now.

Time Series Databases as a Stream Processing Solution

Stream processing is querying data continuously as it streams in from the input source. Stream processing makes real-time analysis possible by unifying applications with analysis. Time-series databases are also capable of processing streaming data, unlike traditional databases. They can manage huge volumes of data and detect anomalies for a particular time and analyze data coming from multiple streams at a time. Stream processing helps get insights in a much faster time frame than other solutions.

Many present businesses are completely based on time series stream processing, which cannot be performed singularly by any other database. We would need to club multiple technologies to work as one to process a time series database with any other database.


Time-series databases are great for horizontally scaling data but are often discouraged when the data scales vertically. Real-time analytics have organizations with applications that use time-series databases as their backbone. Most of the time series database customers span over IoT monitoring, DevOps monitoring, and real-time analytics. Streaming real-time data using time series databases lower real-time analytics costs and increases overall efficiency.

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