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Assertions serve as the building blocks of <a href="/docs/managed-datahub/observe/data-contract">Data Contracts</a> – this is how we verify the contract is met.</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="how-to-create-and-run-assertions">How to Create and Run Assertions<a href="#how-to-create-and-run-assertions" class="hash-link" aria-label="Direct link to How to Create and Run Assertions" title="Direct link to How to Create and Run Assertions"></a></h2><p>Data quality tests (a.k.a. assertions) can be created and run by DataHub Cloud or ingested from a 3rd party tool.</p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="datahub-cloud-observe">DataHub Cloud Observe<a href="#datahub-cloud-observe" class="hash-link" aria-label="Direct link to DataHub Cloud Observe" title="Direct link to DataHub Cloud Observe"></a></h3><p>For DataHub-provided assertion runners, we can deploy an agent in your environment to hit your sources and DataHub. DataHub Cloud Observe offers out-of-the-box evaluation of the following kinds of assertions:</p><ul><li><a href="/docs/managed-datahub/observe/freshness-assertions">Freshness</a> (SLAs)</li><li><a href="/docs/managed-datahub/observe/volume-assertions">Volume</a></li><li><a href="/docs/managed-datahub/observe/custom-sql-assertions">Custom SQL</a></li><li><a href="/docs/managed-datahub/observe/column-assertions">Column</a></li></ul><h3 class="anchor anchorWithStickyNavbar_LWe7" id="anomaly-detection">Anomaly detection<a href="#anomaly-detection" class="hash-link" aria-label="Direct link to Anomaly detection" title="Direct link to Anomaly detection"></a></h3><p>There are many cases where either you do not have the time to figure out what a good rule for an assertion is, or strict rules simply do not suffice for your data validation needs. Traditional rule-based assertions can become inadequate when dealing with complex data patterns or large-scale operations.</p><p><strong>Common Scenarios</strong></p><p>Here are some typical situations where manual assertion rules fall short:</p><p>• <strong>Seasonal data patterns</strong> - A table whose row count changes exhibit weekly seasonality may need a different set of assertions for each day of the week, making static rules impractical to maintain.</p><p>• <strong>Statistical complexity across large datasets</strong> - Figuring out what the expected standard deviation is for each column can be incredibly time consuming and not feasible across hundreds of tables, especially when each table has unique characteristics.</p><p>• <strong>Dynamic data environments</strong> - When data patterns evolve over time, manually updating assertion rules becomes a maintenance burden that can lead to false positives or missed anomalies.</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="the-smart-assertion-solution">The Smart Assertion Solution<a href="#the-smart-assertion-solution" class="hash-link" aria-label="Direct link to The Smart Assertion Solution" title="Direct link to The Smart Assertion Solution"></a></h2><p>In these scenarios, you may want to consider creating a <a href="/docs/managed-datahub/observe/smart-assertions">Smart Assertion</a> to let machine learning automatically detect the normal patterns in your data and alert you when anomalies occur. This approach allows for more flexible and adaptive data quality monitoring without the overhead of manual rule maintenance.</p><p>Both traditional and smart assertions can be defined through the DataHub API or the UI.</p><p align="center"><img loading="lazy" width="70%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/assertions/assertion-ui.png" class="img_ev3q"></p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="reporting-from-3rd-party-tools">Reporting from 3rd Party tools<a href="#reporting-from-3rd-party-tools" class="hash-link" aria-label="Direct link to Reporting from 3rd Party tools" title="Direct link to Reporting from 3rd Party tools"></a></h3><p>You can integrate 3rd party tools as follows:</p><ul><li><a href="/docs/generat
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The <a href="/docs/managed-datahub/observe/data-health-dashboard">Data Health Dashboard</a> provides a birds-eye view of the health of your data landscape. You can slice and dice the data to find the exact answers you're looking for.</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="cost">Cost<a href="#cost" class="hash-link" aria-label="Direct link to Cost" title="Direct link to Cost"></a></h2><p>We provide a plethora of ways to run your assertions, aiming to allow you to use the cheapest possible means to do so and/or the most accurate means to do so, depending on your use case. For example, for Freshness (SLA) assertions, it is relatively cheap to use either their Audit Log or Information Schema as a means to run freshness checks, and we support both of those as well as Last Modified Column, High Watermark Column, and DataHub Operation (<a href="/docs/managed-datahub/observe/freshness-assertions#3-change-source">see the docs for more details</a>).</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="execution-details---where-and-how">Execution details - Where and How<a href="#execution-details---where-and-how" class="hash-link" aria-label="Direct link to Execution details - Where and How" title="Direct link to Execution details - Where and How"></a></h2><p>There are a few ways DataHub Cloud assertions can be executed:</p><ol><li>Directly query the source system:
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a. <code>Information Schema</code> tables are used by default to power cheap, fast checks on a table's freshness or row count.
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b. <code>Audit log</code> or <code>Operation log</code> tables can be used to granularly monitor table operations.
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c. The table itself can also be queried directly. This is useful for freshness checks referencing <code>last_updated</code> columns, row count checks targetting a subset of the data, and column value checks. We offer several optimizations to reduce query costs for these checks.</li><li>Reference DataHub profiling information
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a. <code>Operation</code>s that are reported via ingestion or our SDKs can power monitoring table freshness.
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b. <code>DatasetProfile</code> and <code>SchemaFieldProfile</code> ingested or reported via SDKs can power monitoring table metrics and column metrics.</li></ol><h3 class="anchor anchorWithStickyNavbar_LWe7" id="privacy-execute-in-network-avoid-exposing-data-externally">Privacy: Execute In-Network, avoid exposing data externally<a href="#privacy-execute-in-network-avoid-exposing-data-externally" class="hash-link" aria-label="Direct link to Privacy: Execute In-Network, avoid exposing data externally" title="Direct link to Privacy: Execute In-Network, avoid exposing data externally"></a></h3><p>As a part of DataHub Cloud, we offer a <a href="/docs/managed-datahub/operator-guide/setting-up-remote-ingestion-executor">Remote Executor</a> deployment model. If this model is used, assertions will execute within your network, and only the results will be sent back to DataHub Cloud. Neither your actual credentials, nor your source data will leave your network.</p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="source-system-selection">Source system selection<a href="#source-system-selection" class="hash-link" aria-label="Direct link to Source system selection" title="Direct link to Source system selection"></a></h3><p>Assertions will execute queries using the same source system that was used to initially ingest the table.
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There are some scenarios where customers may have multiple ingestion sources for, i.e. a BigQuery table. In this case, by default the executor will take the ingestion source that was used to ingest the table's <code>DatasetProperties</code>. This behavior can be modified by your customer success rep.</p></div><footer class="theme-doc-footer docusaurus-mt-lg"><div class="slackUtm_uoBr"><div class="slackUtm_uoBr"><hr>Need more help? Join the conversation in <a href="https://datahub.com/slack?utm_source=docs&utm_medium=footer&utm_campaign=docs_footer&utm_content=docs/managed-datahub/observe/assertions">Slack!</a></div></div><div class="theme-doc-footer-edit-meta-row row"><div class="col"><a href="https://github.com/datahub-project/datahub/blob/master/docs/managed-datahub/observe/assertions.md" target="_blank" rel="noreferrer noopener" class="theme-edit-this-page"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_Z9Sw" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div><div class="col lastUpdated_VsjB"></div></div></footer><div class="feedbackWrapper_mUHF"><div class="feedbackWidget_PX4d"><div class="feedbackButtons_wn3V"><strong>Is this page helpful?</strong><div><button class="feedbackButton_UgQs"><span role="img" aria-label="like" class="anticon anticon-like"><svg viewBox="64 64 896 896" focusable="false" data-icon="like" width="1em" height="1em" fill="currentColor" aria-hidden="true"><path d="M885.9 533.7c16.8-22.2 26.1-49.4 26.1-77.7 0-44.9-25.1-87.4-65.5-111.1a67.67 67.67 0 00-34.3-9.3H572.4l6-122.9c1.4-29.7-9.1-57.9-29.5-79.4A106.62 106.62 0 00471 99.9c-52 0-98 35-111.8 85.1l-85.9 311H144c-17.7 0-32 14.3-32 32v364c0 17.7 14.3 32 32 32h601.3c9.2 0 18.2-1.8 26.5-5.4 47.6-20.3 78.3-66.8 78.3-118.4 0-12.6-1.8-25-5.4-37 16.8-22.2 26.1-49.4 26.1-77.7 0-12.6-1.8-25-5.4-37 16.8-22.2 26.1-49.4 26.1-77.7-.2-12.6-2-25.1-5.6-37.1zM184 852V568h81v284h-81zm636.4-353l-21.9 19 13.9 25.4a56.2 56.2 0 016.9 27.3c0 16.5-7.2 32.2-19.6 43l-21.9 19 13.9 25.4a56.2 56.2 0 016.9 27.3c0 16.5-7.2 32.2-19.6 43l-21.9 19 13.9 25.4a56.2 56.2 0 016.9 27.3c0 22.4-13.2 42.6-33.6 51.8H329V564.8l99.5-360.5a44.1 44.1 0 0142.2-32.3c7.6 0 15.1 2.2 21.1 6.7 9.9 7.4 15.2 18.6 14.6 30.5l-9.6 198.4h314.4C829 418.5 840 436.9 840 456c0 16.5-7.2 32.1-19.6 43z"></path></svg></span></button><button class="feedbackButton_UgQs"><span role="img" aria-label="dislike" class="anticon anticon-dislike"><svg viewBox="64 64 896 896" focusable="false" data-icon="dislike" width="1em" height="1em" fill="currentColor" aria-hidden="true"><path d="M885.9 490.3c3.6-12 5.4-24.4 5.4-37 0-28.3-9.3-55.5-26.1-77.7 3.6-12 5.4-24.4 5.4-37 0-28.3-9.3-55.5-26.1-77.7 3.6-12 5.4-24.4 5.4-37 0-51.6-30.7-98.1-78.3-118.4a66.1 66.1 0 00-26.5-5.4H144c-17.7 0-32 14.3-32 32v364c0 17.7 14.3 32 32 32h129.3l85.8 310.8C372.9 889 418.9 924 470.9 924c29.7 0 57.4-11.8 77.9-33.4 20.5-21.5 31-49.7 29.5-79.4l-6-122.9h239.9c12.1 0 23.9-3.2 34.3-9.3 40.4-23.5 65.5-66.1 65.5-111 0-28.3-9.3-55.5-26.1-77.7zM184 456V172h81v284h-81zm627.2 160.4H496.8l9.6 198.4c.6 11.9-4.7 23.1-14.6 30.5-6.1 4.5-13.6 6.8-21.1 6.7a44.28 44.28 0 01-42.2-32.3L329 459.2V172h415.4a56.85 56.85 0 0133.6 51.8c0 9.7-2.3 18.9-6.9 27.3l-13.9 25.4 21.9 19a56.76 56.76 0 0119.6 43c0 9.7-2.3 18.9-6.9 27.3l-13.9 25.4 21.9 19a56.76 56.76 0 0119.6 43c0 9.7-2.3 18.9-6.9 27.3l-14 25.5 21.9 19a56.76 56.76 0 0119.6 43c0 19.1-11 37.5-28.8 48.4z"></path></svg></span></button></div></div></div></div></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/docs/category/features"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Features</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/docs/managed-datahub/observe/column-assertions"><div class="pagination-nav__sublabel">Next</div><div class="paginatio
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