How Cloud Time Series Databases Benefit IoT

Chait Diwadkar
Chait Diwadkar
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Internet of Things (IoT) technologies have played an integral role in the digital transformation of industrial plants, energy and water facilities, transportation systems, and numerous other areas. With leaps in sensor technology and connectivity, end users have increasingly integrated IoT devices with everything from industrial machinery and smart buildings to agriculture networks and power grids.

As the benefits of data-driven decision-making became clearer, adoption of IoT tools proliferated. At the same time, IoT technologies continued to progress and IoT applications expanded further. The amount of connected devices and sensors soared.

Data captured by IoT technologies holds a trove of value. But to best seize it, organizations need a data management platform with advanced analytics that is purpose-built for IoT data. The cloud time series database (TSDB) has evolved as a specialized solution catering to these demands, empowering end-users to extract crucial intelligence from IoT data to make informed decisions.  

For instance, manufacturing facilities with IoT devices deployed across production lines can use a cloud time series database to gain key insights into their operations — providing the knowledge to improve performance, identify potential issues, boost efficiency, and drive productivity and cost savings.

Harnessing the Full Potential of IoT Data

Today, IoT data collection is ubiquitous and the volume of data being generated is still rising. In the coming years, it is projected to be bigger yet. All this data — and the ability for companies to capture more of it — presents both opportunities and challenges. With more data points, organizations can improve their accuracy in terms of identifying trends, leading to deeper insights into processes and patterns. Additionally, it can provide better predictive analytics. But it also demands greater storage capabilities and powerful data processing and analytics to extract meaningful information from vast data sets.

Cloud time series databases are also purpose-built to address these requirements. They are designed specifically to handle large-scale sums of streaming time-series data inputs — allowing organizations to realize the full potential of their data. This is accomplished in several ways:

First, cloud time series databases are tailored to the unique characteristics of IoT-generated data, with storage structures and indexing optimized for timestamped data. Massive time-series data sets are efficiently stored in a compressed format, which enables faster access to data, rapid retrieval times, and improved performance for conducting queries and data analytics.

Cloud time series databases are also equipped with a system architecture for ingesting and processing a continuous stream of high-volume data from IoT-connected devices, making them ideal for real-time monitoring applications that require immediate analysis and alerts. And by storing IoT data according to timestamps with chronological sorting, cloud time series databases are uniquely designed to run complex queries over very large datasets for analyzing historical trends.

Importantly, cloud time series databases are designed to scale to meet the ever-rising storage and performance demands of expanding time-stamped data volumes — ensuring that the database architecture can keep pace with a continuously evolving IoT ecosystem.

Real-World Use Cases

To better understand the practical benefits of a cloud time series database with an IoT data collection program, let’s explore two hypothetical — yet realistic — use cases.

Predictive Maintenance for EV Parts Manufacturing

A manufacturing facility makes automotive parts for electric vehicles (EVs). To improve the reliability of its production equipment and minimize downtime, the facility implements a predictive maintenance program using IoT sensors deployed on critical machinery throughout the production floor. A cloud time series database is used to store and process streaming data collected from the IoT tools.  

Applying data analytics, the facility monitors its operations in real-time and sends alerts if any anomalies in equipment performance are detected, enabling immediate corrective actions. Timestamped historical IoT data stored in the cloud time series database is analyzed to identify warning signs of equipment failure, informing predictive maintenance strategies and optimizing the maintenance schedule. These actions help reduce unplanned downtime while extending the life of assets. The database solution can automatically scale to accommodate future IoT expansions. 

Potable Water Reuse Optimization

A newly built water reuse plant uses advanced water treatment processes to purify treated wastewater for augmenting local supplies. To ensure that the treatment system is optimized and is consistently producing purified water that meets local drinking water standards, the plant installs a network of IoT sensors in the water intake system to monitor water quality parameters.

Collected IoT data is routed to a cloud time series database for storage and real-time analysis to detect any abnormalities in the feedwater quality. The IoT platform is integrated with the plant’s automated control system. If any water quality deviations are identified, automated adjustments to the treatment process are triggered, allowing the plant to produce consistent and safe supplies that comply with regulatory requirements.

  • Chait Diwadkar
    Chait Diwadkar

    Chait Diwadkar is Director of Solution Engineering at TDengine. Prior to joining TDengine he was in the biotechnology industry in technical marketing, professional services, and product management roles and supported customers in pharma, medical devices and diagnostics on analytical chemistry, and genetic platforms.