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If you are interested in learning more about <strong>DataHub Cloud Observe</strong> or trying it out, please <a href="https://datahub.com/products/data-observability/" target="_blank" rel="noopener noreferrer">visit our website</a>.</p></blockquote><h2 class="anchor anchorWithStickyNavbar_LWe7" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction"></a></h2><p>Can you remember a time when the meaning of Data Warehouse Table that you depended on fundamentally changed, with little or no notice?
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If the answer is yes, how did you find out? We'll take a guess - someone looking at an internal reporting dashboard or worse, a user using your your product, sounded an alarm when
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a number looked a bit out of the ordinary. Perhaps your table initially tracked purchases made on your company's e-commerce web store, but suddenly began to include purchases made
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through your company's new mobile app.</p><p>There are many reasons why an important Table on Snowflake, Redshift, BigQuery, or Databricks may change in its meaning - application code bugs, new feature rollouts,
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changes to key metric definitions, etc. Often times, these changes break important assumptions made about the data used in building key downstream data products
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like reporting dashboards or data-driven product features.</p><p>What if you could reduce the time to detect these incidents, so that the people responsible for the data were made aware of data
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issues <em>before</em> anyone else? With DataHub Cloud <strong>Volume Assertions</strong>, you can.</p><p>DataHub Cloud allows users to define expectations about the normal volume, or size, of a particular warehouse Table,
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and then monitor those expectations over time as the table grows and changes.</p><p>In this article, we'll cover the basics of monitoring Volume Assertions - what they are, how to configure them, and more - so that you and your team can
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start building trust in your most important data assets.</p><p>Let's get started!</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="support">Support<a href="#support" class="hash-link" aria-label="Direct link to Support" title="Direct link to Support"></a></h2><p>Volume Assertions are currently supported for:</p><ol><li>Snowflake</li><li>Redshift</li><li>BigQuery</li><li>Databricks</li><li>DataHub Dataset Profile (collected via ingestion)</li></ol><p>Note that an Ingestion Source <em>must</em> be configured with the data platform of your choice in DataHub Cloud's <strong>Ingestion</strong>
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tab.</p><blockquote><p>Note that Volume Assertions are not yet supported if you are connecting to your warehouse
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using the DataHub CLI.</p></blockquote><h2 class="anchor anchorWithStickyNavbar_LWe7" id="what-is-a-volume-assertion">What is a Volume Assertion?<a href="#what-is-a-volume-assertion" class="hash-link" aria-label="Direct link to What is a Volume Assertion?" title="Direct link to What is a Volume Assertion?"></a></h2><p>A <strong>Volume Assertion</strong> is a configurable Data Quality rule used to monitor a Data Warehouse Table
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for unexpected or sudden changes in "volume", or row count. Volume Assertions can be particularly useful when you have frequently-changing
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Tables which have a relatively stable pattern of growth or decline.</p><p>For example, imagine that we work for a company with a Snowflake Table that stores user clicks collected from our e-commerce website.
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This table is updated with new data on a specific cadence: once per hour (In practice, daily or even weekly are also common).
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In turn, there is a downstream Business Analytics Dashboard in Looker that shows important metrics like
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the number of people clicking our "Daily Sale" banners, and this dashboard is generated from data stored in our "clicks" table.
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It is important that our clicks Table is updated with the correct number of rows each hour, else it could mean
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that our downstream metrics dashboard becomes incorrect. The risk of this situation is obvious: our organization
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may make bad decisions based on incomplete information.</p><p>In such cases, we can use a <strong>Volume Assertion</strong> that checks whether the Snowflake "clicks" Table is growing in an expected
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way, and that there are no sudden increases or sudden decreases in the rows being added or removed from the table.
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If too many rows are added or removed within an hour, we can notify key stakeholders and begin to root cause before the problem impacts stakeholders of the data.</p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="anatomy-of-a-volume-assertion">Anatomy of a Volume Assertion<a href="#anatomy-of-a-volume-assertion" class="hash-link" aria-label="Direct link to Anatomy of a Volume Assertion" title="Direct link to Anatomy of a Volume Assertion"></a></h3><p>At the most basic level, <strong>Volume Assertions</strong> consist of a few important parts:</p><ol><li>An <strong>Evaluation Schedule</strong></li><li>A <strong>Volume Condition</strong></li><li>A <strong>Volume Source</strong></li></ol><p>In this section, we'll give an overview of each.</p><h4 class="anchor anchorWithStickyNavbar_LWe7" id="1-evaluation-schedule">1. Evaluation Schedule<a href="#1-evaluation-schedule" class="hash-link" aria-label="Direct link to 1. Evaluation Schedule" title="Direct link to 1. Evaluation Schedule"></a></h4><p>The <strong>Evaluation Schedule</strong>: This defines how often to check a given warehouse Table for its volume. This should usually
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be configured to match the expected change frequency of the Table, although it can also be less frequently depending
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on the requirements. You can also specify specific days of the week, hours in the day, or even
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minutes in an hour.</p><h4 class="anchor anchorWithStickyNavbar_LWe7" id="2-volume-condition">2. Volume Condition<a href="#2-volume-condition" class="hash-link" aria-label="Direct link to 2. Volume Condition" title="Direct link to 2. Volume Condition"></a></h4><p>The <strong>Volume Condition</strong>: This defines the type of condition that we'd like to monitor, or when the Assertion
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should result in failure.</p><p>There are a 2 different categories of conditions: <strong>Total</strong> Volume and <strong>Change</strong> Volume.</p><p><em>Total</em> volume conditions are those which are defined against the point-in-time total row count for a table. They allow you to specify conditions like:</p><ol><li><strong>Table has too many rows</strong>: The table should always have less than 1000 rows</li><li><strong>Table has too few rows</strong>: The table should always have more than 1000 rows</li><li><strong>Table row count is outside a range</strong>: The table should always have between 1000 and 2000 rows.</li></ol><p><em>Change</em> volume conditions are those which are defined against the growth or decline rate of a table, measured between subsequent checks
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of the table volume. They allow you to specify conditions like:</p><ol><li><strong>Table growth is too fast</strong>: When the table volume is checked, it should have < 1000 more rows than it had during the previous check.</li><li><strong>Table growth is too slow</strong>: When the table volume is checked, it should have > 1000 more rows than it had during the previous check.</li><li><strong>Table growth is outside a range</strong>: When the table volume is checked, it should have between 1000 and 2000 more rows than it had during the previous check.</li></ol><p>For change volume conditions, both <em>absolute</em> row count deltas and relative percentage deltas are supported for identifying
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table that are following an abnormal pattern of growth.</p><h4 class="anchor anchorWithStickyNavbar_LWe7" id="3-volume-source">3. Volume Source<a href="#3-volume-source" class="hash-link" aria-label="Direct link to 3. Volume Source" title="Direct link to 3. Volume Source"></a></h4><p>The <strong>Volume Source</strong>: This is the mechanism that DataHub Cloud should use to determine the table volume (row count). The supported
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source types vary by the platform, but generally fall into these categories:</p><ul><li><p><strong>Information Schema</strong>: A system Table that is exposed by the Data Warehouse which contains live information about the Databases
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and Tables stored inside the Data Warehouse, including their row count. It is usually efficient to check, but can in some cases be slightly delayed to update
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once a change has been made to a table.</p></li><li><p><strong>Query</strong>: A <code>COUNT(*)</code> query is used to retrieve the latest row count for a table, with optional SQL filters applied (depending on platform).
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This can be less efficient to check depending on the size of the table. This approach is more portable, as it does not involve
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system warehouse tables, it is also easily portable across Data Warehouse and Data Lake providers.</p></li><li><p><strong>DataHub Dataset Profile</strong>: The DataHub Dataset Profile aspect is used to retrieve the latest row count information for a table.
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Using this option avoids contacting your data platform, and instead uses the DataHub Dataset Profile metadata to evaluate Volume Assertions.
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Note if you have not configured an ingestion source through DataHub, then this may be the only option available.</p></li></ul><p>Volume Assertions also have an off switch: they can be started or stopped at any time with the click of button.</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="creating-a-volume-assertion">Creating a Volume Assertion<a href="#creating-a-volume-assertion" class="hash-link" aria-label="Direct link to Creating a Volume Assertion" title="Direct link to Creating a Volume Assertion"></a></h2><h3 class="anchor anchorWithStickyNavbar_LWe7" id="prerequisites">Prerequisites<a href="#prerequisites" class="hash-link" aria-label="Direct link to Prerequisites" title="Direct link to Prerequisites"></a></h3><ol><li><p><strong>Permissions</strong>: To create or delete Volume Assertions for a specific entity on DataHub, you'll need to be granted the
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<code>Edit Assertions</code> and <code>Edit Monitors</code> privileges for the entity. This will be granted to Entity owners as part of the <code>Asset Owners - Metadata Policy</code>
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by default.</p></li><li><p>(Optional) <strong>Data Platform Connection</strong>: In order to create a Volume Assertion that queries the source data platform directly (instead of DataHub metadata), you'll need to have an <strong>Ingestion Source</strong> configured to your
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Data Platform: Snowflake, BigQuery, or Redshift under the <strong>Integrations</strong> tab.</p></li></ol><p>Once these are in place, you're ready to create your Volume Assertions!</p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="steps">Steps<a href="#steps" class="hash-link" aria-label="Direct link to Steps" title="Direct link to Steps"></a></h3><ol><li>Navigate to the Table that to monitor for volume</li><li>Click the <strong>Quality</strong> tab</li></ol><p align="left"><img loading="lazy" width="80%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/freshness/profile-validation-tab.png" class="img_ev3q"></p><ol start="3"><li>Click <strong>+ Create Assertion</strong></li></ol><p align="left"><img loading="lazy" width="45%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/volume/assertion-builder-volume-choose-type.png" class="img_ev3q"></p><ol start="4"><li><p>Choose <strong>Volume</strong></p></li><li><p>Configure the evaluation <strong>schedule</strong>. This is the frequency at which the assertion will be evaluated to produce a pass or fail result, and the times
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when the table volume will be checked.</p></li><li><p>Configure the evaluation <strong>condition type</strong>. This determines the cases in which the new assertion will fail when it is evaluated.</p></li></ol><p align="left"><img loading="lazy" width="30%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/volume/assertion-builder-volume-condition-type.png" class="img_ev3q"></p><ol start="7"><li>(Optional) Click <strong>Advanced</strong> to customize the volume <strong>source</strong>. This is the mechanism that will be used to obtain the table
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row count metric. Each Data Platform supports different options including Information Schema, Query, and DataHub Dataset Profile.</li></ol><p align="left"><img loading="lazy" width="30%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/volume/assertion-builder-volume-select-source-type.png" class="img_ev3q"></p><ul><li><strong>Information Schema</strong>: Check the Data Platform system metadata tables to determine the table row count.</li><li><strong>Query</strong>: Issue a <code>COUNT(*)</code> query to the table to determine the row count.</li><li><strong>DataHub Dataset Profile</strong>: Use the DataHub Dataset Profile metadata to determine the row count.</li></ul><ol start="8"><li>Configure actions that should be taken when the Volume Assertion passes or fails</li></ol><p align="left"><img loading="lazy" width="40%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/shared/assertion-builder-actions.png" class="img_ev3q"></p><ul><li><p><strong>Raise incident</strong>: Automatically raise a new DataHub <code>Volume</code> Incident for the Table whenever the Volume Assertion is failing. This
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may indicate that the Table is unfit for consumption. Configure Slack Notifications under <strong>Settings</strong> to be notified when
|
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an incident is created due to an Assertion failure.</p></li><li><p><strong>Resolve incident</strong>: Automatically resolved any incidents that were raised due to failures in this Volume Assertion. Note that
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any other incidents will not be impacted.</p></li></ul><ol start="9"><li><p>Click <strong>Next</strong> and provide a description.</p></li><li><p>Click <strong>Save</strong>.</p></li></ol><p>And that's it! DataHub will now begin to monitor your Volume Assertion for the table.</p><p>Once your assertion has run, you will begin to see Success or Failure status for the Table</p><p align="left"><img loading="lazy" width="45%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/volume/profile-passing-volume-assertions-expanded.png" class="img_ev3q"></p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="anomaly-detection-with-smart-assertions-">Anomaly Detection with Smart Assertions ⚡<a href="#anomaly-detection-with-smart-assertions-" class="hash-link" aria-label="Direct link to Anomaly Detection with Smart Assertions ⚡" title="Direct link to Anomaly Detection with Smart Assertions ⚡"></a></h2><p>As part of the <strong>DataHub Cloud Observe</strong> module, DataHub Cloud also provides <strong>Smart Assertions</strong> out of the box. These are
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dynamic, AI-powered Volume Assertions that you can use to monitor the volume of important warehouse Tables, without
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requiring any manual setup.</p><p>You can create smart assertions by simply selecting the <code>Detect with AI</code> option in the UI:</p><p align="left"><img loading="lazy" width="90%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/volume/volume-smart-assertion.png" class="img_ev3q"></p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="stopping-a-volume-assertion">Stopping a Volume Assertion<a href="#stopping-a-volume-assertion" class="hash-link" aria-label="Direct link to Stopping a Volume Assertion" title="Direct link to Stopping a Volume Assertion"></a></h2><p>In order to temporarily stop the evaluation of the assertion:</p><ol><li>Navigate to the <strong>Quality</strong> tab of the Table with the assertion</li><li>Click <strong>Volume</strong> to open the Volume Assertion assertions</li><li>Click the "Stop" button for the assertion you wish to pause.</li></ol><p align="left"><img loading="lazy" width="25%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/shared/stop-assertion.png" class="img_ev3q"></p><p>To resume the assertion, simply click <strong>Start</strong>.</p><p align="left"><img loading="lazy" width="25%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/shared/start-assertion.png" class="img_ev3q"></p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="creating-volume-assertions-via-api">Creating Volume Assertions via API<a href="#creating-volume-assertions-via-api" class="hash-link" aria-label="Direct link to Creating Volume Assertions via API" title="Direct link to Creating Volume Assertions via API"></a></h2><p>Under the hood, DataHub Cloud implements Volume Assertion Monitoring using two concepts:</p><ul><li><p><strong>Assertion</strong>: The specific expectation for volume, e.g. "The table was changed int the past 7 hours"
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or "The table is changed on a schedule of every day by 8am". This is the "what".</p></li><li><p><strong>Monitor</strong>: The process responsible for evaluating the Assertion on a given evaluation schedule and using specific
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mechanisms. This is the "how".</p></li></ul><p>Note that to create or delete Assertions and Monitors for a specific entity on DataHub, you'll need the
|
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<code>Edit Assertions</code> and <code>Edit Monitors</code> privileges for it.</p><h4 class="anchor anchorWithStickyNavbar_LWe7" id="graphql">GraphQL<a href="#graphql" class="hash-link" aria-label="Direct link to GraphQL" title="Direct link to GraphQL"></a></h4><p>In order to create or update a Volume Assertion, you can use the <code>upsertDatasetVolumeAssertionMonitor</code> mutation.</p><h5 class="anchor anchorWithStickyNavbar_LWe7" id="examples">Examples<a href="#examples" class="hash-link" aria-label="Direct link to Examples" title="Direct link to Examples"></a></h5><p>To create a Volume Assertion Entity that verifies that the row count for a table is between 10 and 20 rows, and runs every 8 hours:</p><div class="language-graphql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#bfc7d5;--prism-background-color:#292d3e"><div class="codeBlockContent_biex"><pre tabindex="0" class="prism-code language-graphql codeBlock_bY9V thin-scrollbar"><code class="codeBlockLines_e6Vv"><span class="token-line" style="color:#bfc7d5"><span class="token keyword" style="font-style:italic">mutation</span><span class="token plain"> </span><span class="token definition-mutation function" style="color:rgb(130, 170, 255)">upsertDatasetVolumeAssertionMonitor</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(199, 146, 234)">{</span><span class="token plain"></span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain"> </span><span class="token property-query">upsertDatasetVolumeAssertionMonitor</span><span class="token punctuation" style="color:rgb(199, 146, 234)">(</span><span class="token plain"></span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain"> </span><span class="token attr-name" style="color:rgb(255, 203, 107)">input</span><span class="token punctuation" style="color:rgb(199, 146, 234)">:</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(199, 146, 234)">{</span><span class="token plain"></span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain"> </span><span class="token attr-name" style="color:rgb(255, 203, 107)">entityUrn</span><span class="token punctuation" style="color:rgb(199, 146, 234)">:</span><span class="token plain"> </span><span class="token description string" style="color:rgb(195, 232, 141)">"</span><span class="token description string language-markdown tag punctuation" style="color:rgb(199, 146, 234)"><</span><span class="token description string language-markdown tag" style="color:rgb(255, 85, 114)">urn</span><span class="token description string language-markdown tag" style="color:rgb(255, 85, 114)"> </span><span class="token description string language-markdown tag attr-name" style="color:rgb(255, 203, 107)">of</span><span class="token description string language-markdown tag" style="color:rgb(255, 85, 114)"> </span><span class="token description string language-markdown tag attr-name" style="color:rgb(255, 203, 107)">entity</span><span class="token description string language-markdown tag" style="color:rgb(255, 85, 114)"> </span><span class="token description string language-markdown tag attr-name" style="color:rgb(255, 203, 107)">being</span><span class="token description string language-markdown tag" style="color:rgb(255, 85, 114)"> </span><span class="token description string language-markdown tag attr-name" style="color:rgb(255, 203, 107)">monitored</span><span class="token description string language-markdown tag punctuation" style="color:rgb(199, 146, 234)">></span><span class="token description string" style="color:rgb(195, 232, 141)">"</span><span class="token plain"></span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain"> </span><span class="token attr-name" style="color:rgb(255, 203, 107)">type</span><span class="token punctuation" style="color:rgb(199, 146, 234)">:</span><span class="token plain"> </span><span class="token constant" style="color:r
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The supported operator types are <code>GREATER_THAN</code>, <code>GREATER_THAN_OR_EQUAL_TO</code>, <code>LESS_THAN</code>, <code>LESS_THAN_OR_EQUAL_TO</code>, and <code>BETWEEN</code> (requires minValue, maxValue).
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The supported parameter types are <code>NUMBER</code>.</p><p>You can use same endpoint with assertion urn input to update an existing Volume Assertion and corresponding Monitor:</p><div class="language-graphql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#bfc7d5;--prism-background-color:#292d3e"><div class="codeBlockContent_biex"><pre tabindex="0" class="prism-code language-graphql codeBlock_bY9V thin-scrollbar"><code class="codeBlockLines_e6Vv"><span class="token-line" style="color:#bfc7d5"><span class="token keyword" style="font-style:italic">mutation</span><span class="token plain"> </span><span class="token definition-mutation function" style="color:rgb(130, 170, 255)">upsertDatasetVolumeAssertionMonitor</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(199, 146, 234)">{</span><span class="token plain"></span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain"> </span><span class="token property-query">upsertDatasetVolumeAssertionMonitor</span><span class="token punctuation" style="color:rgb(199, 146, 234)">(</span><span class="token plain"></span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain"> </span><span class="token attr-name" style="color:rgb(255, 203, 107)">assertionUrn</span><span class="token punctuation" style="color:rgb(199, 146, 234)">:</span><span class="token plain"> </span><span class="token description string" style="color:rgb(195, 232, 141)">"</span><span class="token description string language-markdown tag punctuation" style="color:rgb(199, 146, 234)"><</span><span class="token description string language-markdown tag" style="color:rgb(255, 85, 114)">urn</span><span class="token description string language-markdown tag" style="color:rgb(255, 85, 114)"> </span><span class="token description string language-markdown tag attr-name" style="color:rgb(255, 203, 107)">of</span><span class="token description string language-markdown tag" style="color:rgb(255, 85, 114)"> </span><span class="token description string language-markdown tag attr-name" style="color:rgb(255, 203, 107)">assertion</span><span class="token description string language-markdown tag" style="color:rgb(255, 85, 114)"> </span><span class="token description string language-markdown tag attr-name" style="color:rgb(255, 203, 107)">created</span><span class="token description string language-markdown tag" style="color:rgb(255, 85, 114)"> </span><span class="token description string language-markdown tag attr-name" style="color:rgb(255, 203, 107)">in</span><span class="token description string language-markdown tag" style="color:rgb(255, 85, 114)"> </span><span class="token description string language-markdown tag attr-name" style="color:rgb(255, 203, 107)">earlier</span><span class="token description string language-markdown tag" style="color:rgb(255, 85, 114)"> </span><span class="token description string language-markdown tag attr-name" style="color:rgb(255, 203, 107)">query</span><span class="token description string language-markdown tag punctuation" style="color:rgb(199, 146, 234)">></span><span class="token description string" style="color:rgb(195, 232, 141)">"</span><span class="token plain"></span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain"> </span><span class="token attr-name" style="color:rgb(255, 203, 107)">input</span><span class="token punctuation" style="color:rgb(199, 146, 234)">:</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(199, 146, 234)">{</span><span class="token plain"></span><br></span><span class="token-line" style="color:#bfc7d5"><span class="token plain"> </span><span class="token attr-name" style="color:rgb(255, 203, 107)">entityUrn</span><span class="token punctuation" style="color:rgb(199, 146, 234)">:</span><span class="token plain"> </span><span class="token description string" style="color:rgb(195, 232, 141)">"</span><span class="token description string language-markdown ta
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