Transforming IoT Data into Actionable Insights with Time Series Insights

Time Series Insights for IoT data:

Generally, IoT data typically consist of time series data, which makes sense when observed over a period of time, like a sensor’s behavioral change, etc. Billions of data is getting generated from IoT these days, and it needs to be stored in a repository. But it’s challenging to store this data in a way, where you want to use it in near real-time to be processed to derive meaningful insights when needed in machine-critical situations. So, we need to store this data in a way that it makes sense. This calls for a service that can scale massively and help operators find insights quickly, Azure Time Series Insights.

Introduction to Azure Time Series Insights:

Azure Time Series Insights is a serverless, fully managed data analytics solution (PaaS), that users can use to integrate with their constantly changing data like data from several sensors or machines, data from airlines, satellites, etc. Any data that can be generated on a large scale and needs to be analyzed can be used through Azure Time Series Insights.

Azure Time Series Insights architecture:

The above figure shows a high-level architecture of how Azure TSI can be implemented in a real-life scenario. Time series real time data can be generated by various sources like satellites, mobile devices, medical devices, sensors, etc. Azure IoT Hub or Event hubs can be used to fetch the data from these devices into the Azure environment. Further, this data can be processed using services such as Stream analytics, Logic apps and Azure functions and computed signals from the processing pipeline are pushed to Azure Time Series Insights for storing and analytics. Once in the Time series insights platform, the data can be used for visualization. The data can also be queried and aggregated accordingly. In additional, customers can also leverage existing analytics and machine learning capabilities on top of the data available in Time Series Insights platform. Data from Time Series insights can be further processed using Databricks and pre-trained machine learning (ML) models can be applied to offer predictions in real time.

Components of Azure Time Series Insights:

  1. Integration: Time Series Insights provides easy integration for the data generated by IoT devices by allowing connection between the cloud gateways like IoT hub and Event hubs. Data from these can be easily consumed in JSON structures, cleaned and stored in columnar store.
  2. Storage: Azure TSI also takes care of the data that is to be retained in the system for querying and visualizing the data. By default, data is stored on solid state drives (SSDs) for fast retrieval and can be retained for upto 400 days.
  3. Data visualization: Another component of Azure TSI, data visualization helps data fetched from multiple data sources and stored in the columnar stores, to be visualized in the form of line charts or heat maps.
  4. Query Service: Time Series Insights also provides a query service using which you can integrate Time Series Insights into your custom applications.

 

Conclusion:

Azure Time Series Insights helps you to easily connect to billions of events in Azure IoT hub or Event hubs, visualize and analyze those events to spot the anomalies and discover hidden trends in your data. It can both store as well as visualize the data. Alternatively, one can also have the capabilities to run queries against this data and obtain more simplified results.

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