OPC UA and Google BigQuery Integration
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Table of Contents
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
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Input and output integration overview
<p>The OPC UA plugin provides an interface for retrieving data from OPC UA server devices, facilitating effective data collection and monitoring.</p>
<p>The Google BigQuery plugin allows Telegraf to write metrics to Google Cloud BigQuery, enabling robust data analytics capabilities for telemetry data.</p>
Integration details
OPC UA
<p>The OPC UA Plugin retrieves data from devices that communicate using the OPC UA protocol, allowing you to collect and monitor data from your OPC UA servers.</p>
Google BigQuery
<p>The Google BigQuery plugin for Telegraf enables seamless integration with Google Cloud’s BigQuery service, a popular data warehousing and analytics platform. This plugin facilitates the transfer of metrics collected by Telegraf into BigQuery datasets, making it easier for users to perform analyses and generate insights from their telemetry data. It requires authentication through a service account or user credentials and is designed to handle various data types, ensuring that users can maintain the integrity and accuracy of their metrics as they are stored in BigQuery tables. The configuration options allow for customization around dataset specifications and handling metrics, including the management of hyphens in metric names, which are not supported by BigQuery for streaming inserts. This plugin is particularly useful for organizations leveraging the scalability and powerful query capabilities of BigQuery to analyze large volumes of monitoring data.</p>
Configuration
OPC UA
Google BigQuery
Input and output integration examples
OPC UA
<ol> <li> <p><strong>Basic Configuration</strong>: Set up the plugin with your OPC UA server endpoint and desired metrics. This allows Telegraf to start gathering metrics from the configured nodes.</p> </li> <li> <p><strong>Node ID Setup</strong>: Use the configuration to specify specific nodes, such as temperature sensors, to monitor their values in real-time. For example, configure node <code>ns=3;s=Temperature</code> to gather temperature data directly.</p> </li> <li> <p><strong>Group Configuration</strong>: Simplify monitoring multiple nodes by grouping them under a single configuration—this sets defaults for all nodes in that group, thereby reducing redundancy in setup.</p> </li> </ol>
Google BigQuery
<ol> <li> <p><strong>Real-Time Analytics Dashboard</strong>: Leverage the Google BigQuery plugin to feed live metrics into a custom analytics dashboard hosted on Google Cloud. This setup would allow teams to visualize performance data in real-time, providing insights into system health and usage patterns. By using BigQuery’s querying capabilities, users can easily create tailored reports and dashboards to meet their specific needs, thus enhancing decision-making processes.</p> </li> <li> <p><strong>Cost Management and Optimization Analysis</strong>: Utilize the plugin to automatically send cost-related metrics from various services into BigQuery. Analyzing this data can help businesses identify unnecessary expenses and optimize resource usage. By performing aggregation and transformation queries in BigQuery, organizations can create accurate forecasts and manage their cloud spending efficiently.</p> </li> <li> <p><strong>Cross-Team Collaboration on Monitoring Data</strong>: Enable different teams within an organization to share their monitoring data using BigQuery. With the help of this Telegraf plugin, teams can push their metrics to a central BigQuery instance, fostering collaboration. This data-sharing approach encourages best practices and cross-functional awareness, leading to collective improvements in system performance and reliability.</p> </li> <li> <p><strong>Historical Analysis for Capacity Planning</strong>: By using the BigQuery plugin, companies can collect and store historical metrics data essential for capacity planning. Analyzing trends over time can help anticipate system needs and scale infrastructure proactively. Organizations can create time-series analyses and identify patterns that inform their long-term strategic decisions.</p> </li> </ol>
Feedback
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Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
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