Azure Time Series Insights
Author: Ronald Fung
Creation Date: 2 June 2023
Next Modified Date: 2 June 2024
A. Introduction
Note
The Time Series Insights (TSI) service will no longer be supported after March 2025. Consider migrating existing TSI environments to alternative solutions as soon as possible. For more information on the deprecation and migration, visit our documentation.
Caution
This is a Gen1 article.
This article describes the features and options for the Azure Time Series Insights Gen1 Explorer web app. The Azure Time Series Insights Explorer demonstrates the powerful data visualization capabilities provided by the service and can be accessed within your own environment.
Azure Time Series Insights is a fully managed analytics, storage, and visualization service that makes it simple to explore and analyze billions of IoT events simultaneously. It gives you a global view of your data, which lets you quickly validate your IoT solution and avoid costly downtime to mission-critical devices. You can discover hidden trends, spot anomalies, and conduct root-cause analyses in near real time.
B. How is it used at Seagen
As a biopharma research company using Microsoft Azure, you can use Azure Time Series Insights to analyze and visualize time-series data from your research efforts. Here are some ways you can use Azure Time Series Insights:
Real-time data analysis: Azure Time Series Insights provides real-time data analysis capabilities, allowing you to analyze and visualize data as it is generated by sensors, devices, and other sources.
Historical data analysis: Azure Time Series Insights provides historical data analysis capabilities, allowing you to analyze and visualize data from past research efforts.
Data visualization: Azure Time Series Insights provides powerful data visualization capabilities, allowing you to create custom charts, graphs, and dashboards to help you analyze and understand your research data.
Data exploration: Azure Time Series Insights provides data exploration capabilities, allowing you to easily explore and analyze large volumes of time-series data.
Anomaly detection: Azure Time Series Insights provides anomaly detection capabilities, allowing you to identify and investigate anomalies in your research data.
Predictive analytics: Azure Time Series Insights provides predictive analytics capabilities, allowing you to predict future trends and events based on past research data.
Integration with Azure services: Azure Time Series Insights integrates with other Azure services, such as Azure IoT Hub and Azure Stream Analytics, allowing you to easily manage and analyze your research data.
Overall, by leveraging Azure Time Series Insights, you can effectively analyze and visualize time-series data from your research efforts, and make data-driven decisions that support your biopharma research goals. By using Azure Time Series Insights for real-time and historical data analysis, data visualization, data exploration, anomaly detection, predictive analytics, and integration with other Azure services, you can effectively manage your research data and gain insights into your biopharma research efforts.
C. Features
Azure Time Series Insights is a fully managed, scalable, and secure analytics service for time-series data. It provides a range of features to help you analyze and visualize your data, including:
Real-time data analysis: Azure Time Series Insights provides real-time data analysis capabilities, allowing you to analyze and visualize data as it is generated by sensors, devices, and other sources.
Historical data analysis: Azure Time Series Insights provides historical data analysis capabilities, allowing you to analyze and visualize data from past research efforts.
Data visualization: Azure Time Series Insights provides powerful data visualization capabilities, allowing you to create custom charts, graphs, and dashboards to help you analyze and understand your research data.
Data exploration: Azure Time Series Insights provides data exploration capabilities, allowing you to easily explore and analyze large volumes of time-series data.
Anomaly detection: Azure Time Series Insights provides anomaly detection capabilities, allowing you to identify and investigate anomalies in your research data.
Predictive analytics: Azure Time Series Insights provides predictive analytics capabilities, allowing you to predict future trends and events based on past research data.
Integration with Azure services: Azure Time Series Insights integrates with other Azure services, such as Azure IoT Hub and Azure Stream Analytics, allowing you to easily manage and analyze your research data.
Security and compliance: Azure Time Series Insights provides advanced security and compliance features, including role-based access control, encryption, and compliance certifications.
Scalability: Azure Time Series Insights is highly scalable, allowing you to easily scale up or down as your data needs change.
Cost-effectiveness: Azure Time Series Insights provides a cost-effective analytics solution, allowing you to optimize your analytics costs based on your data access patterns.
Overall, Azure Time Series Insights provides a powerful and flexible analytics solution for time-series data, with features that enable real-time and historical data analysis, data visualization, data exploration, anomaly detection, predictive analytics, integration with other Azure services, security and compliance, scalability, and cost-effectiveness. By leveraging these features, you can effectively manage your research data and make data-driven decisions that support your biopharma research efforts.
D. Where Implemented
E. How it is tested
Testing Azure Time Series Insights involves verifying that the service is properly configured and that it is effectively analyzing and visualizing your time-series data. Here are some steps you can take to test Azure Time Series Insights:
Verify configuration: Verify that Azure Time Series Insights is properly configured and integrated with your Azure account and applications and websites.
Test data ingestion: Test Azure Time Series Insights by ingesting time-series data from sensors, devices, and other sources, ensuring that the service is effectively collecting and storing your data.
Test data analysis: Test Azure Time Series Insights by analyzing and visualizing your time-series data, ensuring that the service is effectively analyzing and visualizing your data for real-time and historical analysis.
Test data exploration: Test Azure Time Series Insights by exploring and analyzing large volumes of time-series data, ensuring that the service is effectively managing and analyzing large datasets.
Test data visualization: Test Azure Time Series Insights by creating custom charts, graphs, and dashboards to help you analyze and understand your research data.
Test anomaly detection: Test Azure Time Series Insights by identifying and investigating anomalies in your research data, ensuring that the service is effectively detecting and alerting you to potential issues.
Test predictive analytics: Test Azure Time Series Insights by predicting future trends and events based on past research data, ensuring that the service is effectively providing predictive analytics capabilities.
Test security: Test Azure Time Series Insights by verifying that the service provides advanced security features, helping to protect the privacy and security of your data.
Test scalability: Test Azure Time Series Insights by verifying that the service is highly scalable, allowing you to easily scale up or down as your data needs change.
Test cost-effectiveness: Test Azure Time Series Insights by verifying that the service provides a cost-effective analytics solution, allowing you to optimize your analytics costs based on your data access patterns.
Overall, testing Azure Time Series Insights involves verifying that the service is effectively analyzing and visualizing your time-series data, and providing powerful data ingestion, data analysis, data exploration, data visualization, anomaly detection, predictive analytics, security, scalability, and cost-effectiveness capabilities. By taking these steps, you can ensure that you are effectively using Azure Time Series Insights to manage your research data and make data-driven decisions that support your biopharma research efforts.
F. 2023 Roadmap
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G. 2024 Roadmap
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H. Known Issues
As with any software or service, there may be known issues or limitations with Azure Time Series Insights that users should be aware of. Here are some of the known issues with Azure Time Series Insights:
Ingestion rate limitations: Azure Time Series Insights has some limitations on the ingestion rate of time-series data, which may impact its suitability for high-volume data ingestion needs.
Data retention limitations: Azure Time Series Insights has limitations on the length of time that data can be retained, which may impact its suitability for certain data retention needs.
Data access limitations: Access to data stored in Azure Time Series Insights is limited, with retrieval times and costs based on the access patterns.
Data exploration limitations: Azure Time Series Insights has limitations on the depth of data exploration, which may impact its suitability for certain data exploration needs.
Cost optimization: Optimizing storage costs in Azure Time Series Insights can be complex, requiring careful management of data access patterns and retention policies.
Data processing limitations: Azure Time Series Insights has some limitations on the types of data processing that can be performed, which may impact its suitability for certain data processing needs.
Integration limitations: Integration with other Azure services may be limited, depending on the specific services being used.
Overall, while Azure Time Series Insights offers a powerful and flexible analytics solution for time-series data, users must be aware of these known issues and take steps to mitigate their impact. This may include carefully managing data ingestion rates, data retention policies, data exploration needs, storage costs, data processing needs, and integration with other Azure services. By taking these steps, users can ensure that they are effectively using Azure Time Series Insights to manage their research data and make data-driven decisions that support their biopharma research efforts.
[x] Reviewed by Enterprise Architecture
[x] Reviewed by Application Development
[x] Reviewed by Data Architecture