In today’s data-driven world, the ability to collect, store, and analyze data has become a cornerstone for industries ranging from IoT and industrial automation to finance and healthcare. Whether monitoring the performance of a smart factory, tracking stock market fluctuations, or analyzing website traffic, time-series data plays a pivotal role in uncovering trends, predicting outcomes, and making informed decisions. This has given rise to a new class of database management systems — the time-series database (TSDB) — specifically designed to handle the unique challenges and make the most of the opportunities presented by this data.
What Is Time-Series Data?
A time series is data recorded “as measurements or observations of events as a function of the time at which they occurred.” At its most basic, time-series data is simply a measurement and the time at which it was taken. For example, an air temperature of 30 degrees taken at 8 a.m. is a time-series data point, and a collection of temperature readings, taken at the same location in hourly intervals, is a time series.
Some common examples of industrial time-series data are given as follows:
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In the renewable energy industry, wind speed, energy production, and battery discharge rates
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In the manufacturing industry, vibration levels, temperature, and pressure of industrial machines
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In the logistics industry, vehicle location, speed, and fuel consumption
Although simple in concept, time-series data has become a key component of data analysis in many industries. Time-series data is useful because it can answer questions about trends, patterns, and correlation over time. The ability to see how a measurement changes over a period of time enables powerful insight in a wide variety of areas ranging from business trends to the operating status of industrial equipment. In addition, as the cost of communication continues to decrease and smart devices and sensors become commonplace, more time-series data is being generated than ever before.
However, in order to make use of this data — to monitor devices, generate reports, trigger alarms, make predictions, and more — businesses need data platforms that can handle its scale. The amount of time-series data generated is growing to an extent that even traditional analysis is becoming difficult for legacy data historians and general-purpose relational databases. Instead, forward-thinking businesses are increasingly adopting the purpose-built time-series database as the platform for processing their time-series data.
What Is a Time-Series Database?
A time-series database is a database management system that is optimized to store, process, and analyze time-series data. It incorporates features such as data compression to reduce storage costs, time-based indexing for faster querying, and built-in support for time-specific calculations, such as averages or trends over specified periods. By leveraging a time-series database, organizations can gain deeper insights into time-dependent patterns, optimize operations, and respond to events in real time.
General-purpose databases, such as relational databases like Oracle Database, Microsoft SQL Server, and MySQL, or NoSQL databases like MongoDB, are designed to manage a wide variety of data structures and workloads, from transactional records to unstructured documents. While these systems are versatile, they are not optimized for the unique challenges posed by time-series data. Time-series databases are much more efficient in terms of ingestion rate, query latency, and storage costs.
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Data ingestion rate: In many time-series data scenarios, millions of data points are produced every second and need to be ingested in real time. Relational databases are not designed to handle this amount of data, and while NoSQL databases can be scaled to handle it, the amount of resources required quickly becomes prohibitive.
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Query latency: Time-series applications often need to scan a huge number of data points to get an aggregation result, which can result in high latency. For example, it would take hours for a general-purpose database to calculate the average response time of all clicks on Amazon.com, by which time the aggregation result would be outdated.
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Storage costs: Internet-connected devices and applications are generating data nonstop around the clock — sometimes exceeding a terabyte in a single day. Because relational and NoSQL databases cannot efficiently store and compress this data, storage costs can become very high very fast.
These issues mainly involve efficiency in processing large datasets, but there are also areas where general-purpose databases often do not support the basic requirements of time-series applications:
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Data lifecycle management: Once time-series data ages out, it is generally removed in batches, not one data point at a time.
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Roll-up: Time-series data is rolled up based on a specified time window and saved into new table. In addition, raw data and rolled-up data may have different lifecycles and retention policies.
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Special analytics functions: Besides the functions provided by general databases, time-series applications need functions like interpolation, downsampling, time-weighted average, moving average, and cumulative sum.
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Continuous query: Time-series applications run queries in the background periodically over a sliding time window in order to populate dashboards, generate reports, and downsample data sets.
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Session and state windows: Aggregation and analytic functions may be run over a session or state window, not just time – for example, consider a function that calculates average power consumption only when a machine is in the running state.
With general databases, developers are forced to write custom code to implement these features, leading to increased technical debt and development costs, especially in industries that do not often have the luxury of large, dedicated development teams.
Time-series databases, being built to purpose, natively provide all these functions and more. More importantly, because they process only time-series data, they can be fully adapted to the needs of industrial datasets to offer superior performance and efficiency.
What Are the Characteristics of Industrial Time-Series Data?
Time-series databases are able to offer significantly higher performance than other database management systems because they take advantage of the characteristics of industrial time-series datasets. These characteristics are listed as follows:
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Timestamp: The generation of time-series data is triggered by a predefined timer or event, and when devices collect time-series data, a timestamp is always associated with each record. Time-series data can therefore be indexed by timestamp; the timestamp associated with each data record is the key for computing or analysis.
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Structure: Time-series data generated by devices is always structured, often having a predefined data type or fixed length.
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Stream-like nature: Time-series data from connected devices can be thought of as a data stream, being continuously collected and flowing into the database. These data streams are independent from each other.
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Stability: Although the scale is large, overall traffic in a time-series scenario will remain stable and can be predicted and calculated given the number of devices and the sampling period.
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Trend focus: Trends over time are more important than values at any specific time, and essentially the same analysis results can be obtained even if some data points are lost. For time-series data, the main challenges are storing, processing, and analyzing data sets due to their massive scale.
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High write/read ratio: In general, raw time-series data is only read occasionally by analytics software and other similar tools to generate reports and run algorithms, but it is written constantly to the database.
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Immutability: Specific time-series data records are almost never updated or deleted. Time-series data generated by devices can be considered append-only, similar to log files.
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Retention policy: In most scenarios, a lifecycle is defined for the collected time-series data, after which it is deleted to reduce storage costs. It is rare that raw data is stored forever.
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Real-time computing: To meet business requirements, data processing systems must be able to perform operations on time-series data in real time – for example, a monitoring system must be able to trigger as soon as the conditions for an alarm are met.
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Aggregation: Most queries are performed on a specified time range, not all historical time-series data. In addition, data aggregation is always needed over a time window for all or a subset of devices. Filtering a subset of devices for aggregation is mandatory in IoT applications.
In addition to making use of these characteristics, time-series databases can also save resources by eliminating mechanisms that are not necessary for its specialized use cases. For example, database transactions are not required for time-series data, unlike typical relational database use cases.
Why Are Time-Series Databases Becoming Popular?
Time-series databases are not new: they have been widely used in the financial and process industries for decades.
However, they are becoming popular now mainly due to the rapid growth of the IoT. As more and more devices are Internet-connected and constantly sending data — time-series, of course — to the cloud, an increasing number of sectors are becoming interested in purpose-built time-series databases. As production modernizes and control systems evolve into the Industrial Internet of Things (IIoT), the industrial applications of time-series data are becoming evident as well. Finally, IT infrastructure has been steadily expanding, and everything from servers, containers, and network devices to apps and microservices is being monitored, which also generates massive amounts of time-series data.
Technologically speaking, older time-series databases and data historians are often closed systems that use outdated architectures, and they cannot scale to support the growing volume of data. In the old days, a million time-series data points seemed like a huge number, but now millions and even billions of data points is nothing out of the ordinary. Furthermore, integrating legacy time-series solutions with popular data analysis tools like artificial intelligence and machine learning frameworks is difficult, if not impossible. These legacy systems cannot be moved to the cloud without significant effort, and their licensing models are no longer acceptable for modern applications.
The growing market and the limitations of older time-series databases leave space for a new generation of time-series databases. Over the past 10 years, at least 20 new time-series databases have been released on the market, with open-source time-series databases becoming particularly popular.
How to Choose a Time-Series Database
The following ten criteria can help you select the best time-series database for your business:
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Open source: You don’t want to build your system on a black box, especially when there are many open-source products available. In addition to transparency, open-source products also have better ecosystems and developer communities and prevent vendor lock-in.
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Performance: All time-series databases perform better than general databases when processing time-series data, but some have an issue with high cardinality, meaning that performance deteriorates when the number of metrics in the database gets higher. Also, some time-series database management systems experience unacceptable latency when accessing historical data. When you select a time-series database, make sure that it performs well with a data set similar in size to what you’ll have in production – not just now but in the future as well.
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Scalability: As your business grows, your data will too – that’s why the best time-series database solutions need horizontal scalability. This is a weak spot for many current solutions, and even InfluxDB, the most popular time-series database, locks scalability away in its enterprise edition.
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Query language: SQL is still the most popular query language among database management systems: it’s powerful, fast, and already known by millions of developers and administrators. However, some time-series databases use proprietary query languages instead of SQL. This makes these systems more difficult to learn, even for experienced users, and greatly increases the cost of migrating from a traditional database.
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Ecosystem: Considering the number of devices and sensors that generate time-series data, the best time-series database solutions need to provide connectors in major programming languages in addition to REST APIs. Different methods for data ingestion as well as integration with a variety of visualization and BI tools are also essential.
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Cloud native: It won’t be long before many systems, including time-series databases, are running in the cloud. For that reason a cloud-native time-series database is the most future-ready choice, though you should ensure that your solution is really cloud-native, not just “cloud-ready.”
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Extra features: Modern data platforms do more than just store data. You need a time-series database solution that supports features like continuous queries, caching, stream processing, and data subscription — otherwise, you’ll have to integrate with specialized tools or implement them yourself, and that makes your system more complex and more expensive.
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Out-of-order data: In some time-series databases, like Prometheus, data points that are received out of order cannot be processed and are just thrown away. If out-of-order data may occur in your use case — for example, if your message queue is in the middle of your data path, or simply if you encounter network issues – you need to be sure that your database solution can handle that data.
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System footprint: Depending on where and how your data is collected, such as on the edge, you might not be able to deploy a large-scale system and instead need a lightweight solution.
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Monitoring: The best time-series database solutions provide good observability as well as integration with monitoring tools like Grafana — otherwise, you won’t be able to know whether issues have occurred until it’s too late.
Conclusion
In a world where data volume and complexity continue to grow, understanding the fundamentals of time-series databases is more essential than ever. By leveraging time-series databases, businesses can stay ahead of the curve, adapting to dynamic environments with scalable, high-performance solutions tailored to their unique needs. With rising demand for analyzing real-time data and long-term trends, adopting a time-series database can be a strategic move that ensures agility and competitiveness in the digital era.
As industrial enterprises modernize data infrastructure to prepare for digital transformation and Industry 4.0, selecting the most appropriate data systems is critical to ensure that business growth is not bottlenecked today or ten years down the road. Different data workloads require different database solutions — one size does not fit all. For time-series data, no matter the size of your data set, a purpose-built time-series database is the best tool for the job.
