AI & Data Analytics

Picking the best time-series database is similar to choosing any database, but there are a few key distinctions. Selecting a new (time series) database might be tricky at times. Though the option may appear to be agonizing at times, the best way to overcome this challenge  is to arm yourself with as much knowledge as possible before deciding.  

A time series database stores and tracks data change over time from the Internet of Things, with frequent sensor readings coming in from devices. A time series database deals with time-stamped metrics, events, and measurements. The correct time series database can make it much easier and cheaper to store your data. Making the right database choice can also make it easier to query and analyze, empowering you to increase the value of the data. 

Time-series database is the fastest-growing database type in the industry in recent years, thanks to two key factors: usability and scalability. 

 Usability: Having built-in functions and features to analyze trends available at the data-layer  

 Scalability: Time-series data accumulates quickly, and traditional databases cannot handle that massive scale from performance improvements, such as higher ingest rates and faster queries at scale. 

This article will walk you through the technical aspects of selecting the correct time-series database: 

  1. The impact of data type: Even though we're talking about time-series databases here, it doesn't guarantee that all data will be the same. Data delivered for analysis from IoT devices, for example, must be treated differently from financial data used to make forecasts. 

The shortest delay you may define in a given time unit is time precision. For example, if you set a delay to 100ps, you may provide 10ps as the precision for measuring that delay or how many decimal points to use relative to the time unit supplied. 

Thus, time precision also plays a vital role in handling the data from varied sources. If you are seeking time precision as a critical parameter, you can choose a system where you can tune it to match your changing needs. 

  1. Storage: Time series databases may grow enormous. The database may receive data from many devices and systems, and this number will change over time. They also communicate data regularly, which might vary depending on the time of day or the device.  

The storage solution you choose must be able to manage large amounts of data and frequent entries without losing data points. Compressing or downsampling saves storage space.  

For example, we collect data for monitoring purposes from IoT devices; we can gather 10,000 data points every day. This data is collected every second this number keeps on adding up as the monitoring system increases. You can see the impact on storage space and the system's performance due to this enormous data size. 

Downsampling is a technique that results in lowering data resolution. You can use downsampling on data or compress it to optimize your storage space. Separate storage bins with varying downsampling ratios are standard practice. For example, you might preserve your input data in its entirety for seven days, then compress it by half for storage for another 30 days. Finally, you'd put products into long-term storage that were 50 percent more wrapped. 

Algorithms, unlike downsampling, can accomplish lossless compression. Data gets encoded using compression methods that use more minor bits than the original version. You can utilize the level of redundancy in the encoded data to calculate the compression rate. You must choose a platform with flexible packing options, both lossless and lossy, to switch as per your requirements. 

  1. Scaling and Clustering: 

The accumulation of time-series data is rapid. The amount of data that a single linked automobile may collect in an hour is 25GB. Relational databases and NoSQL databases cannot manage the size of time series data, which is why a time-series-optimized database will always beat them. 

In an IoT database, you may start with receiving data from 100 devices, and later you may need to support data from a thousand or more devices. With time-series databases, you can handle scale by adding efficiencies that one can only achieve when you treat time as a first-class citizen, which is what time-series databases do. Here is where scalability is essential. 

Performance gains include increased ingest rates, quicker queries at scale (although some handle more than others), and improved data compression. A database that supports both vertical and horizontal scaling is advisable. 

  1. Reading speed Requirements: 

An important aspect is retrieving large volumes of data at the desired speed when you need it. If you want to utilize your data for real-time analytics, machine learning, or artificial intelligence, you'll need a database that can read data rapidly and effectively. 

The more complex and extensive the database, the longer it takes to acquire data. Using aggregation and compression techniques will improve read access speed.  

Compared to application data, the writing of time series data is steadier.  

The application data is generally proportionate to the page view of the application, with peaks and troughs. Time series data generate at a given time frequency with no further constraints. The data generating pace is relatively consistent. Individual objects create time-series data on their own. 

Consider an in-memory time-series database. In-memory databases automatically compress source data, reducing query search time. As almost all-time series databases are in-memory, you'll get one anyhow.  

  1. System Footprint size:  

Depending on your data collecting process, a database with fewer features and a smaller footprint may be more cost-effective. For example, a mobile analysis device (not necessarily a phone) should include a remote database that receives and analyses raw data before sending aggregated or analyzed data back to the primary office. 

Current time-series databases are small and easy to interface with logging and other systems. If footprint size is not a problem, there are plenty of options. 

  1. Logging and monitoring the database:  

Server logs are one of the most basic and visible instances of time series data, although application monitoring may take numerous forms.  

You can track server metrics to determine peak and trough consumption, allowing system administrators to better plan capacity and spot abnormalities. A mobile application's telemetry can be kept in a time series database so that developers can analyze use trends and enhance their programs. 


Time series database is a very vast area. We may not be able to pinpoint the best one right away, but the factors described in this article can help you decide based on your specific requirements. 

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Picking the best time-series database is similar to choosing any database, but there are a few key distinctions. Selecting a new (time series) database might be tricky at times.

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