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Invite your colleague(s) to your Soda Cloud organization so they can access the newly-deployed Soda Agent to connect to data sources and begin monitoring data quality. In your Soda Cloud account, navigate to your avatar > Invite Team Members and fill in the blanks.
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If your Soda Admin has integrated your Soda Cloud account with Slack or MS Teams to receive check notifications, you can direct anomaly dashboard alerts to those channels. The dashboard does not support sending alerts via webhook.
About this guide Set up a Soda Agent Automate data quality monitoring Access an anomaly dashboard Set up alert notifications Go further
During its initial scan of your datasource, Soda Cloud discovers all the datasets the data source contains. It captures basic information about each dataset, including a datasetâs schema and the columns it contains.
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For example, if you want to receive notifications any time Soda detects an anomalous volume of duplicate values in an order_id column, click the Duplicate tile to display all the columns for which Soda automatically detects anomalies, then click the bell for order_id and set up a rule. If you also wish to receive notifications for anomalous volumes of missing values in the same column, click the Missing tile, then click the bell for order_id to set up a second rule.
A self-hosted Soda Agent is a tool that empowers Soda Cloud users to securely access data sources to scan for data quality. Create a Kubernetes cluster in a cloud services provider environment, then use Helm to deploy a self-hosted Soda Agent in the cluster.
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In the editing panel, provide the connection configurations Soda Cloud needs to be able to access the data in the data source. Connection configurations are data source-specific and include values for things such as a databaseâs host and access credentials.
For a Dataset Metric, click the bell to follow the guided instructions to set up a rule that defines where to send an alert notification when Soda detects an anomalous measurement for the metric.
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In the editing panel, provide details that Soda Cloud uses to determine which datasets to include or exclude when it profiles the columns in a dataset. The default syntax in the editing panel instructs Soda to profile every column of every dataset in this data source, and, superfluously, all datasets with names that begin with prod. The % is a wildcard character. See Add column profiling for more detail on profiling syntax.
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When you click a Column Metrics tile to access more information, the list below details which columns contained anomalies.
After approximately five days, during which Sodaâs machine learning studies your data, you can navigate to the Anomalies tab on the Dataset page on one of the datasets you included in profiling to view the issues Soda automatically detected.
This example offers instructions for both a self-hosted and Soda-hosted agent deployment models which use Soda Cloud connected to a Soda Agent to securely access data sources and execute scheduled scans for data quality anomaly detections. See: Choose a flavor of Soda.
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The anomaly dashboard adheres to Sodaâs âno noiseâ policy when it comes to alert notifications for data quality issues. As such, the dashboard does not automatically send any notifications to anyone out of the box. If you wish to received alert notifications for any of the anomalies the dashboard detects, use the bell (ð) icon.
Access the data source-specific connection configurations for the connection syntax and descriptions; adjust the values to correspond with your data sourceâs details.
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To gather more detailed profile information about datasets in your data source and automatically build an anomaly dashboard for data quality observability, you can configure Soda Cloud to profile the columns in datasets.
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This setup provides a secure, out-of-the-box Soda-hosted Agent to manage access to data sources from within your Soda Cloud account.
To more securely provide sensitive values such as usernames and passwords in a self-hosted agent deployment model, use environment variables in a values.yml file when you deploy the Soda Agent. See Use environment variables for data source connection credentials for details.
Use this guide to set up Soda and begin automatically monitoring the data quality of datasets in a data source. Use the guided workflow in Soda Cloud to connect to a data source, profile your data, and activate anomaly dashboards for your datasets.
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For a Column Metric, click the bell next to an individual column name from those listed in the table below the three column metric tiles. Follow the guided instructions to set up a rule that defines where to send an alert notification when Soda detects an anomalous measurement for the metric.
This guide offers Data Analysts, Data Scientists, and business users instructions to set up Soda to profile and begin monitoring data for quality, right out of the box.
In the editing panel, specify the datasets that Soda Cloud must include or exclude from this basic discovery activity. The default syntax in the editing panel instructs Soda to collect basic dataset information from all datasets in the data source except those with names that begin with test_. The % is a wildcard character. See Add dataset discovery for more detail on profiling syntax.
The three Dataset Metrics tiles represent the most recent measurement or, in other words, one dayâs worth of data anomaly detection. The three Column Metrics tiles display the last seven daysâ worth of measurements and any anomalies that Soda detected.
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Column profiling can be resource-heavy, so carefully consider the datasets for which you truly need column profile information. Refer to Compute consumption and cost considerations for more detail.
Click a Dataset Metric tile or the column name for a Column Metric to open the Check History for the anomaly detection check. Optionally, you can add feedback to individual data points in the check history graph to help refine the anomaly detectionâs algorithm pattern recognition and its ability to recognize anomalies.
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