Viewing datastream issues
This guide explains how to view datastream issues.
Introduction
There are two types of issues in Adverity:
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Errors
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Errors signal critical issues and stop processing the data.
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Warnings
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Warnings signal non-critical issues and continue processing the data.
Adverity's datastream issues inform you about potential problems with your data and datastreams. These problems include the following issues:
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Issues that prevent your datastream from fetching data from your chosen data source, such as incorrect datastream configuration settings
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Issues that occur while applying a transformation to your data extract, such as incorrect configuration of a transformation
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Issues detected by data monitors, such as duplicate data rows in your data extracts
Viewing datastream issues
There are a number of ways to stay up-to-date with all the datastream warnings and errors in your Adverity workspace. You can find datastream issues in the following ways:
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Set up notifications so that Adverity will inform you about errors and warnings
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See warnings and errors relating to all datastreams in the task monitor in the Activity page
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Click the Warnings or Failed tab in the overview of a datastream (shown below) to see warnings or errors relating to this datastream
Viewing details of the data quality issues in your data extract
To view the data that does not meet your monitoring rules, follow these steps:
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Open the datastream overview or Activity page and find the task with data quality issues.
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Click the task overview for the task with the Data Quality error or warning.
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Above the data extract, you will see Data Quality errors and warnings for specific monitors.
In the selected data quality issue box above the data extract, click View details. In the details window, you can see the following information:
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Duplicate data checks
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Adverity displays the duplicate rows found in your data extract and the number of times they occur.
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You can download the duplicate rows at the bottom of the window.
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Custom monitors
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Adverity displays the rows in your data extract that violate each of the defined rules and the number of times the violation occurs.
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