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<title>Snowflake Archives - Datagaps | Automated Cloud Data Testing | ETL, BI & BigData</title>
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<title>Snowflake Archives - Datagaps | Automated Cloud Data Testing | ETL, BI & BigData</title>
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<title>Top 3 ETL Testing Tools</title>
<link>https://www.datagaps.com/blog/top-3-etl-testing-tools/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Thu, 05 Jun 2025 17:38:50 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Dataflow]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[ETL Testing]]></category>
<category><![CDATA[Snowflake]]></category>
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<description><![CDATA[<p>ETL Testing refers to the testing, validation, and analysis of the Extraction, Transformation, and Loading Processes that are part of ETL and ELT Pipelines. As ETL testing refers to “Data-in-Motion” Testing, the unit test architecture and principles slightly differ from “Data-at-Rest” Testing (Warehouse/DB Validation).</p>
<p>The post <a href="https://www.datagaps.com/blog/top-3-etl-testing-tools/">Top 3 ETL Testing Tools</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Automated Cloud Data Testing | ETL, BI & BigData</a>.</p>
]]></description>
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<h2 class="elementor-heading-title elementor-size-default">What Is ETL Testing Tools?</h2> </div>
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<p>ETL Testing refers to the testing, validation, and analysis of the Extraction, Transformation, and Loading Processes that are part of ETL and ELT Pipelines. As <a href="https://www.datagaps.com/data-testing-concepts/etl-testing/">ETL testing</a> refers to “Data-in-Motion” Testing, the unit test architecture and principles slightly differ from “Data-at-Rest” Testing (Warehouse/DB Validation).</p><p>For any Top ETL Testing Tool, a typical task list of an ETL Testing Model must include:</p><ul class="custom-list"><li>Data Model Review</li><li>Source Data Testing</li><li>Post-Ingestion Validation</li><li>Post-Transform Validation</li><li>Aggregation Analysis</li><li>Data Compare between Source and Target</li><li>Data Quality and Accuracy Testing in Target</li><li>Data Integrity Examination</li><li>ETL Operational Update Validation</li><li><a href="https://www.datagaps.com/data-testing-concepts/etl-testing/">ETL Performance Testing</a></li></ul><p>As ETL Pipelines contain most of the transformations, relations, and aggregations that will be performed, the majority of errors occur in these sets. Even with a static Database source, as functions get updated and changed the errors can creep up even in a stable ETL pipeline.</p><p>Also read: <span style="color: #339966;"><a style="color: #339966;" href="https://www.datagaps.com/blog/etl-validator-for-data-migration-testing/">ETL Validator for Data Migration Testing</a></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why Is ETL Testing Important?</h2> </div>
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<p>ETL testing is important for several reasons:</p><ul><li><strong>First,</strong> it ensures the integrity and reliability of the data being used in a data warehousing or business intelligence system. By verifying the accuracy and completeness of the data, ETL testing helps to ensure that the decisions made based on that data are correct and accurate.</li><li><strong>Second,</strong> ETL testing helps to identify and resolve any issues or errors in the ETL process. This can prevent data loss and improve the overall performance of the system. For example, if an ETL test detects that certain data is missing or incorrect, the issue can be quickly addressed and corrected, which can improve the quality of the data and the reliability of the system.</li><li><strong>Third,</strong> ETL testing can help to ensure compliance with industry standards and regulations. Many industries have specific requirements for the handling and processing of data, and ETL testing can help to ensure that the data being used in a data warehousing or business intelligence system meets these requirements. This can prevent fines and penalties for non-compliance, and can also help to protect the reputation of the organization.</li></ul><p>Overall, ETL testing is a critical step in the data warehousing and business intelligence process, and it is essential for ensuring the accuracy and reliability of the data being used in these systems. Hence we should more cautious in picking the right ETL Testing Tool.</p> </div>
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<h2 class="elementor-heading-title elementor-size-medium">Here are some Top ETL Testing Tools available in the market.
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<h3 class="elementor-heading-title elementor-size-default">#1 ETL Validator</h3> </div>
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<p><strong><span style="color: #339966;"><a style="color: #339966;" href="https://www.datagaps.com/etl-testing-tools/etl-validator/">DataGaps ETL Validator</a> </span></strong>stands on top when it comes to ETL Testing Automation. This is now part of DataOps Suite.</p> </div>
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<img fetchpriority="high" decoding="async" width="640" height="299" src="https://www.datagaps.com/wp-content/uploads/ETL-Validator-01-1024x479.webp" class="attachment-large size-large wp-image-5333" alt="ETL-Validator-01" srcset="https://www.datagaps.com/wp-content/uploads/ETL-Validator-01-1024x479.webp 1024w, https://www.datagaps.com/wp-content/uploads/ETL-Validator-01-300x140.webp 300w, https://www.datagaps.com/wp-content/uploads/ETL-Validator-01-768x359.webp 768w, https://www.datagaps.com/wp-content/uploads/ETL-Validator-01-1536x718.webp 1536w, https://www.datagaps.com/wp-content/uploads/ETL-Validator-01.webp 1920w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>The DataGaps ETL Validator is a tool that helps organizations ensure the quality and integrity of their data as it is transferred from one system to another through the process of Extract, Transform, and Load (ETL). The ETL Validator checks the data against a set of pre-defined rules and constraints and identifies any errors or inconsistencies that may be present. This can help organizations avoid problems such as incorrect data being loaded into their systems, or data being lost or corrupted during the ETL process.</p><p>One of the key features of the DataGaps ETL Validator is its ability to handle large amounts of data quickly and efficiently. This is important because ETL processes often involve moving large volumes of data from multiple sources, and the Validator can help organizations ensure that their data is transferred accurately and without delays.</p><p>Another important feature of the DataGaps ETL Validator is its ability to identify and highlight any errors or inconsistencies in the data. This can help organizations quickly identify and fix any issues, and ensure that their data is accurate and complete. The Validator also provides detailed reports and logs, which can be used to track the progress of the ETL process and troubleshoot any problems that may arise.</p> </div>
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<img decoding="async" width="640" height="300" src="https://www.datagaps.com/wp-content/uploads/ETL-Validator-02-1024x480.webp" class="attachment-large size-large wp-image-5334" alt="ETL-Validator-02" srcset="https://www.datagaps.com/wp-content/uploads/ETL-Validator-02-1024x480.webp 1024w, https://www.datagaps.com/wp-content/uploads/ETL-Validator-02-300x141.webp 300w, https://www.datagaps.com/wp-content/uploads/ETL-Validator-02-768x360.webp 768w, https://www.datagaps.com/wp-content/uploads/ETL-Validator-02-1536x720.webp 1536w, https://www.datagaps.com/wp-content/uploads/ETL-Validator-02.webp 1920w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>Overall, the DataGaps ETL Validator is a valuable and Top ETL Testing tool for organizations that need to ensure the quality and integrity of their data as it is transferred from one system to another. By providing fast, efficient, and accurate data validation, the Validator can help organizations avoid costly errors and improve the reliability and effectiveness of their ETL processes.</p> </div>
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<p><span style="color: #339966;"><a style="color: #339966;" href="https://www.datagaps.com/etl-validator-trial-request/">ETL Validator – Free Trial</a></span></p> </div>
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<p><span style="color: #339966;"><a style="color: #339966;" href="https://www.datagaps.com/blog/how-to-validate-etl-testing-checklist/">ETL Testing Tool Checklist</a></span></p> </div>
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<p><span style="color: #339966;"><a style="color: #339966;" href="https://www.youtube.com/watch?v=j4rAuW7I7Do" data-wplink-edit="true">ETL Validator – Webinar</a></span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">#2 QuerySurge</h3> </div>
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<p>QuerySurge is a powerful ETL Testing tool designed to help businesses and organizations quickly and efficiently test and validate their data. With its intuitive interface and robust set of features, QuerySurge makes it easy to ensure that your data is accurate, complete, and ready for use.</p><p>One of the key features of QuerySurge ETL Testing Tool is its ability to automatically generate and execute test cases. This means that you can quickly and easily test your data without having to manually write and run individual test cases. QuerySurge also allows you to specify the criteria for each test, so you can tailor your tests to fit the specific needs of your organization.</p><p>Another important feature of QuerySurge is its ability to integrate with a wide range of data sources. This means that you can use QuerySurge to test data from a variety of sources, including databases, flat files, and even web services. This flexibility allows you to easily test data from multiple sources and ensure that your data is consistent and accurate across all of your systems.</p><p>In addition to its automation and data integration capabilities, QuerySurge also offers a number of other powerful features. For example, QuerySurge allows you to define and manage your test data sets, so you can easily reuse test data and maintain a consistent testing environment. QuerySurge also provides detailed reporting capabilities, so you can easily track the progress of your tests and identify any potential issues.</p> </div>
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<img decoding="async" width="640" height="328" src="https://www.datagaps.com/wp-content/uploads/QuerySurge.webp" class="attachment-large size-large wp-image-5517" alt="QuerySurge" srcset="https://www.datagaps.com/wp-content/uploads/QuerySurge.webp 745w, https://www.datagaps.com/wp-content/uploads/QuerySurge-300x154.webp 300w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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Overall, QuerySurge is a valuable tool for anyone looking to efficiently and effectively test and validate their data. With its powerful features and intuitive interface, QuerySurge makes it easy to ensure that your data is accurate and ready for use. </div>
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<h3 class="elementor-heading-title elementor-size-default">#3 iCEDQ</h3> </div>
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<p>iCEDQ is a powerful data quality management tool designed to help businesses and organizations ensure the accuracy and completeness of their data. With its intuitive interface and robust set of features, iCEDQ makes it easy to identify and correct data errors, ensuring that your data is clean and ready for use.</p>
<p>One of the key features of iCEDQ is its ability to automatically identify and flag potential data errors. Using a variety of algorithms and techniques, iCEDQ can quickly and accurately detect errors in your data, such as missing values, incorrect formatting, and inconsistencies. This allows you to quickly and easily identify areas where your data may be incorrect, so you can take action to fix the errors.</p>
In addition to its error detection capabilities, iCEDQ also offers a number of other powerful features. For example, iCEDQ allows you to define and manage your data quality rules, so you can easily ensure that your data meets the specific requirements of your organization. iCEDQ also provides detailed reporting capabilities, so you can track the progress of your data quality efforts and identify areas where you may need to take action. </div>
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<img loading="lazy" decoding="async" width="640" height="266" src="https://www.datagaps.com/wp-content/uploads/iCEDQ.webp" class="attachment-large size-large wp-image-5520" alt="iCEDQ" srcset="https://www.datagaps.com/wp-content/uploads/iCEDQ.webp 748w, https://www.datagaps.com/wp-content/uploads/iCEDQ-300x125.webp 300w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>Overall, iCEDQ is a valuable tool for anyone looking to improve the quality of their data. With its powerful features and intuitive interface, iCEDQ makes it easy to identify and correct errors in your data, ensuring that it is accurate and reliable.</p> </div>
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<p><span style="text-decoration: underline;">Disclaimer</span>: The above-mentioned list is purely an outcome of the conversations and feedback received from various industry users in the ETL/Data Warehouse testing space. Any concerns or views can be shared at <a href="mailto:contact@datagaps.com">contact@datagaps.com</a></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Try ETL Validator testing tool <span style="text-decoration: underline">Free for 14</span> days for your ETL Testing Automation needs. <a href="https://www.datagaps.com/etl-validator-trial-request/">Free Trial</a></h2> </div>
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<p>The post <a href="https://www.datagaps.com/blog/top-3-etl-testing-tools/">Top 3 ETL Testing Tools</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Automated Cloud Data Testing | ETL, BI & BigData</a>.</p>
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<title>ETL Testing In Snowflake Using DataOps Suite</title>
<link>https://www.datagaps.com/blog/etl-testing-in-snowflake-using-dataops-suite/</link>
<comments>https://www.datagaps.com/blog/etl-testing-in-snowflake-using-dataops-suite/#respond</comments>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Tue, 14 Feb 2023 13:18:56 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Data Quality]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[ETL Testing]]></category>
<category><![CDATA[Snowflake]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=11775</guid>
<description><![CDATA[<p>ETL stands for Extract, Transform, and Load. It is the process by which data is extracted from one or more sources, transformed into compatible formats, and then loaded into a target Database or Data Warehouse.</p>
<p>The post <a href="https://www.datagaps.com/blog/etl-testing-in-snowflake-using-dataops-suite/">ETL Testing In Snowflake Using DataOps Suite</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Automated Cloud Data Testing | ETL, BI & BigData</a>.</p>
]]></description>
<content:encoded><![CDATA[ <div data-elementor-type="wp-post" data-elementor-id="11775" class="elementor elementor-11775" data-elementor-post-type="post">
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<h2 class="elementor-heading-title elementor-size-default">Introduction and Overview of ETL Testing Snowflake</h2> </div>
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<p><a href="https://www.ibm.com/in-en/topics/etl">ETL</a> stands for Extract, Transform, and Load. It is the process by which data is extracted from one or more sources, transformed into compatible formats, and then loaded into a target Database or Data Warehouse. The sources may include Flat Files, Third-Party Applications, Databases, etc. <a href="https://www.datagaps.com/data-testing-concepts/etl-testing/">ETL testing</a> is necessary to ensure that data moving from external sources to the data warehouse is accurate at each point between the source and destination.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">Purpose of ETL</h3> </div>
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<p>ETL allows businesses to consolidate data from multiple databases and other sources into a single repository with the data that has been modified and used during the analysis of data. This unified data repository allows for simplified access to analysis and additional processing of the data. There are many advantages of using ETL tools for the migration of data. It reduces delivery time, reduces unnecessary expenses, makes the process easy to use, and also will be simple for data migrations. Data Integration, Data Warehousing, and Data Migration are the three common uses of ETL.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">ETL Testing Process in Snowflake</h3> </div>
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<p>The data will be migrated from one data warehouse to another cloud-based <a href="https://aws.amazon.com/data-warehouse/">data warehouse</a> using various steps present in ETL Testing. The multiple steps involved in this process are the extraction of data, the transformation of the data, and finally the loading of data to the different data sources. This process is essential for proper testing such the quality of data can be checked efficiently. The DataOps Suite tool can be used efficiently for ETL Testing. <a href="https://www.datagaps.com/request-demo/">Request Demo</a></p> </div>
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<h2><strong>The various steps involved in ETL Testing are as follows:</strong></h2> </div>
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<h3 class="elementor-heading-title elementor-size-default">Step 1: Extraction Of Data</h3> </div>
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<p>Data Extraction is the first step that will be performed in the ETL Testing. In this procedure, the data will usually be extracted from the same data source, or it can be extracted from different source locations also. Here, for example, the data is extracted from the same source i.e. Snowflake, and Customer data is extracted. After extracting the data from the source location, then further the data can be transformed according to the client’s requirements.</p> </div>
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<img loading="lazy" decoding="async" width="1320" height="697" src="https://www.datagaps.com/wp-content/uploads/Data-Extraction-From-Customers-Table.png" class="attachment-full size-full wp-image-11776" alt="Data-Extraction-From-Customers-Table" srcset="https://www.datagaps.com/wp-content/uploads/Data-Extraction-From-Customers-Table.png 1320w, https://www.datagaps.com/wp-content/uploads/Data-Extraction-From-Customers-Table-300x158.png 300w, https://www.datagaps.com/wp-content/uploads/Data-Extraction-From-Customers-Table-1024x541.png 1024w, https://www.datagaps.com/wp-content/uploads/Data-Extraction-From-Customers-Table-768x406.png 768w" sizes="(max-width: 1320px) 100vw, 1320px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Data Extraction From Customers Table</figcaption>
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<h3 class="elementor-heading-title elementor-size-default">Step 2: Transformation Of Data</h3> </div>
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<p>After the data is extracted from the same or different data source to the same or the other source, a few changes or transformations in the customers’ data are done. Generally, data transformations include changes in data types or other changes according to the client’s requirements.</p><p>The below screenshot depicts the Customer data that is being transformed.</p> </div>
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<img loading="lazy" decoding="async" width="1321" height="695" src="https://www.datagaps.com/wp-content/uploads/Data-Transformation-Using-SQL-Component.png" class="attachment-full size-full wp-image-11777" alt="Data-Transformation-Using-SQL-Component" srcset="https://www.datagaps.com/wp-content/uploads/Data-Transformation-Using-SQL-Component.png 1321w, https://www.datagaps.com/wp-content/uploads/Data-Transformation-Using-SQL-Component-300x158.png 300w, https://www.datagaps.com/wp-content/uploads/Data-Transformation-Using-SQL-Component-1024x539.png 1024w, https://www.datagaps.com/wp-content/uploads/Data-Transformation-Using-SQL-Component-768x404.png 768w" sizes="(max-width: 1321px) 100vw, 1321px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Data Transformation Using SQL Component</figcaption>
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<p>Once the data is transformed, <strong>data comparison </strong>can be performed to view the changes after transformation.</p> </div>
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<img loading="lazy" decoding="async" width="1320" height="700" src="https://www.datagaps.com/wp-content/uploads/Comparison-Of-Data-Using-Data-Compare-Component.png" class="attachment-full size-full wp-image-11778" alt="Comparison-Of-Data-Using-Data-Compare-Component" srcset="https://www.datagaps.com/wp-content/uploads/Comparison-Of-Data-Using-Data-Compare-Component.png 1320w, https://www.datagaps.com/wp-content/uploads/Comparison-Of-Data-Using-Data-Compare-Component-300x159.png 300w, https://www.datagaps.com/wp-content/uploads/Comparison-Of-Data-Using-Data-Compare-Component-1024x543.png 1024w, https://www.datagaps.com/wp-content/uploads/Comparison-Of-Data-Using-Data-Compare-Component-768x407.png 768w" sizes="(max-width: 1320px) 100vw, 1320px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Comparison Of Data Using Data Compare Component</figcaption>
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<p><span class="fontSizeMediumPlus">Further, the quality of data can be checked by using the <strong>Data Rules Component. </strong></span><span class="fontSizeMediumPlus">Data quality checks are done to find out the issues in the quality of data. The <strong>Data</strong><strong> Profile Component</strong> can also be used to find out the data quality issues.</span></p> </div>
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<p>In the below screenshot, the quality of data is checked by verifying the email address as well as the name string check by using different data rules in the data rules component.</p> </div>
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<img loading="lazy" decoding="async" width="1323" height="696" src="https://www.datagaps.com/wp-content/uploads/Data-Quality-Check-Using-Data-Rules-Component.png" class="attachment-full size-full wp-image-11779" alt="Data-Quality-Check-Using-Data-Rules-Component" srcset="https://www.datagaps.com/wp-content/uploads/Data-Quality-Check-Using-Data-Rules-Component.png 1323w, https://www.datagaps.com/wp-content/uploads/Data-Quality-Check-Using-Data-Rules-Component-300x158.png 300w, https://www.datagaps.com/wp-content/uploads/Data-Quality-Check-Using-Data-Rules-Component-1024x539.png 1024w, https://www.datagaps.com/wp-content/uploads/Data-Quality-Check-Using-Data-Rules-Component-768x404.png 768w" sizes="(max-width: 1323px) 100vw, 1323px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Data Quality Check Using Data Rules Component</figcaption>
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<p>Data profiling is also done to check the quality of data.</p> </div>
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<img loading="lazy" decoding="async" width="1320" height="695" src="https://www.datagaps.com/wp-content/uploads/Profiling-Data-Using-Data-Profile-Component.png" class="attachment-full size-full wp-image-11780" alt="Profiling-Data-Using-Data-Profile-Component" srcset="https://www.datagaps.com/wp-content/uploads/Profiling-Data-Using-Data-Profile-Component.png 1320w, https://www.datagaps.com/wp-content/uploads/Profiling-Data-Using-Data-Profile-Component-300x158.png 300w, https://www.datagaps.com/wp-content/uploads/Profiling-Data-Using-Data-Profile-Component-1024x539.png 1024w, https://www.datagaps.com/wp-content/uploads/Profiling-Data-Using-Data-Profile-Component-768x404.png 768w" sizes="(max-width: 1320px) 100vw, 1320px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Profiling Data Using Data Profile Component</figcaption>
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<h3 class="elementor-heading-title elementor-size-default">Step 3: Loading Of The Data</h3> </div>
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<p>Once the transformation of data is performed, further the data will be loaded from one source to a particular file location. Here the data is loaded by using the <strong>DB Sink component.</strong> This is the general testing process followed in the DataOps Suite tool. <a href="https://www.datagaps.com/request-demo/">Request Demo</a></p> </div>
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<p>The below screenshot depicts the data loaded to the desired data source after the data transformations are done.</p> </div>
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<img loading="lazy" decoding="async" width="1320" height="695" src="https://www.datagaps.com/wp-content/uploads/Data-Loading-Using-DB-Sink-Component.png" class="attachment-full size-full wp-image-11781" alt="Data-Loading-Using-DB-Sink-Component" srcset="https://www.datagaps.com/wp-content/uploads/Data-Loading-Using-DB-Sink-Component.png 1320w, https://www.datagaps.com/wp-content/uploads/Data-Loading-Using-DB-Sink-Component-300x158.png 300w, https://www.datagaps.com/wp-content/uploads/Data-Loading-Using-DB-Sink-Component-1024x539.png 1024w, https://www.datagaps.com/wp-content/uploads/Data-Loading-Using-DB-Sink-Component-768x404.png 768w" sizes="(max-width: 1320px) 100vw, 1320px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Data Loading Using DB Sink Component</figcaption>
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<p>Once the ETL Testing process is completed, the reports generated need to be checked and evaluated as there will be some differences. In our DataOps Suite tool, BI Validator can be used to check and evaluate the reports.</p><p> </p><p><a href="https://www.datagaps.com/data-testing-concepts/data-warehouse-testing-checklist/">Read: Data Warehouse Testing Checklist</a></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">Conclusion</h3> </div>
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<p><a href="https://www.datagaps.com/data-testing-concepts/etl-testing/">ETL testing</a> is an important process when data is transferred from one or multiple databases to another database, especially when a huge amount of data is used. It makes sure that the data loaded in the destination source is accurate enough. The step-by-step procedure of ETL Testing can be checked by using different components in our DataOps Suite tool. By using ETL Testing, the performance can be increased. Once the entire ETL Testing is completed in Snowflake, then finally the reports will be generated. The reports generated will be checked and validated finally by using the <a href="https://www.datagaps.com/bi-validator/">BI Validator</a>.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/etl-testing-in-snowflake-using-dataops-suite/">ETL Testing In Snowflake Using DataOps Suite</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Automated Cloud Data Testing | ETL, BI & BigData</a>.</p>
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<title>Data Validation for Snowflake</title>
<link>https://www.datagaps.com/blog/snowflake-data-migration-testing/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Fri, 18 Feb 2022 14:28:42 +0000</pubDate>
<category><![CDATA[Snowflake]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=7365</guid>
<description><![CDATA[<p>Migration from a legacy data warehouse such as Netezza to a cloud-based Snowflake data warehouse requires multiple steps. Data Validation is the key to the success of data migration projects. Datagaps Data Flow can be used to validate data in each step of the data migration as</p>
<p>The post <a href="https://www.datagaps.com/blog/snowflake-data-migration-testing/">Data Validation for Snowflake</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Automated Cloud Data Testing | ETL, BI & BigData</a>.</p>
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<h5 class="elementor-heading-title elementor-size-default">Migrating to Snowflake Cloud Data Warehouse</h5> </div>
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<p>Migration from a legacy data warehouse such as Netezza to a cloud-based <a href="https://www.snowflake.com/en/fundamentals/data-warehouse/">Snowflake data warehouse</a> requires multiple steps. Data Validation is the key to the success of data migration projects. Datagaps DataFlow can be used to validate data in each step of the data migration as well as the end-to-end data validation scenarios. If you are looking for Snowflake testing tools – <a href="https://www.datagaps.com/etl-testing-tools/etl-validator-download/">Try Dataflow free for 14 days</a> </p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Step 1: Extract data from the Legacy data warehouse</h5> </div>
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<p>Data is typically extracted into CSV or Parquet format and moved to a landing zone in AWS S3. Depending on the data volumes, AWS offers multiple options for moving the files to S3. Once the data has been moved to AWS S3, data validations need to be performed to ensure that all the data was properly extracted and migrated to AWS S3. Since there are not many transformations in this step, these tests are typically one-to-one comparisons of the data in the tables in the legacy data warehouse and the files in the AWS S3 landing zone.</p> </div>
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<p>– Compare table to file row counts<br />– Compare data encoding<br />– Compare data completeness<br />– Compare data values</p> </div>
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<p>A sample test case diagram is shown to the right. JDBC Component can be used to read data from the legacy data warehouse. File Component can be used to read data from AWS S3. Finally, the Data Compare component can be used to compare the two datasets. Sample output for a data comparison component is shown below.</p> </div>
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<img loading="lazy" decoding="async" width="584" height="219" src="https://www.datagaps.com/wp-content/uploads/oracle-to-S3.webp" class="attachment-large size-large wp-image-6400" alt="oracle-to-S3" srcset="https://www.datagaps.com/wp-content/uploads/oracle-to-S3.webp 584w, https://www.datagaps.com/wp-content/uploads/oracle-to-S3-300x113.webp 300w" sizes="(max-width: 584px) 100vw, 584px" /> </div>
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<p>Data comparison test case</p> </div>
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<img loading="lazy" decoding="async" width="640" height="327" src="https://www.datagaps.com/wp-content/uploads/data-compare.webp" class="attachment-large size-large wp-image-6402" alt="data-compare" srcset="https://www.datagaps.com/wp-content/uploads/data-compare.webp 1000w, https://www.datagaps.com/wp-content/uploads/data-compare-300x153.webp 300w, https://www.datagaps.com/wp-content/uploads/data-compare-768x392.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>The output of data comparison</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Step 2: Transform data</h5> </div>
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<p>Transformations such as data type conversions can be performed in this step. Data curation can be also done to improve the <a href="https://www.datagaps.com/dataops-suite/data-quality/">Data Quality</a> before the data is loaded into Snowflake. Before curating the data, it is important to profile the data and run data quality tests to identify data quality issues with the data. DataFlow can be used to perform these tasks.</p> </div>
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<p>– Compare data between landing zone and staging (curated) zone in S3<br />– Use Data Profile and Data Rules components to identify data quality issues<br />– Curate data and sync to the staging zone</p> </div>
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<img loading="lazy" decoding="async" width="640" height="398" src="https://www.datagaps.com/wp-content/uploads/data-profile.webp" class="attachment-large size-large wp-image-6412" alt="data-profile" srcset="https://www.datagaps.com/wp-content/uploads/data-profile.webp 961w, https://www.datagaps.com/wp-content/uploads/data-profile-300x186.webp 300w, https://www.datagaps.com/wp-content/uploads/data-profile-768x477.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<img loading="lazy" decoding="async" width="640" height="284" src="https://www.datagaps.com/wp-content/uploads/data-rules-1024x455.webp" class="attachment-large size-large wp-image-6413" alt="data-rules" srcset="https://www.datagaps.com/wp-content/uploads/data-rules-1024x455.webp 1024w, https://www.datagaps.com/wp-content/uploads/data-rules-300x133.webp 300w, https://www.datagaps.com/wp-content/uploads/data-rules-768x342.webp 768w, https://www.datagaps.com/wp-content/uploads/data-rules.webp 1493w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>Data Rules component</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Step 3: Copy data to Snowflake</h5> </div>
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<p>Assuming that the Snowflake tables have been created, the last step is to copy the data to the snowflake. Use the VALIDATE function to validate the data files and identify any errors. DataFlow can be used to compare the data between the Staging Zone (S3) files and Snowflake after the load.</p> </div>
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<p>– Compare table to file row counts<br />– Compare data encoding<br />– Compare data completeness<br />– Compare data values<br />– End-to-end data validation (Legacy data warehouse to Snowflake)</p> </div>
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<p>DataFlow can be used to perform end-to-end <a href="https://www.datagaps.com/blog/how-to-perform-continuous-data-validation/">Data Validation</a> in a single test as shown to the right. A single DataFlow can be used to compare data between legacy data warehouse and S3 as well as legacy data warehouse and Snowflake.</p> </div>
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<img loading="lazy" decoding="async" width="633" height="338" src="https://www.datagaps.com/wp-content/uploads/End-to-end-data-validation.webp" class="attachment-large size-large wp-image-6423" alt="End-to-end-data-validation" srcset="https://www.datagaps.com/wp-content/uploads/End-to-end-data-validation.webp 633w, https://www.datagaps.com/wp-content/uploads/End-to-end-data-validation-300x160.webp 300w" sizes="(max-width: 633px) 100vw, 633px" /> </div>
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<p>End-to-end test case</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">Step 4: Modify reports to Use Snowflake</h5> </div>
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<p>While snowflake provides JDBC/ODBC drivers and supports most of the commonly used SQL functions, there are going to be some differences between the way reports are developed and executed in the legacy <a href="https://www.datagaps.com/services/data-warehouse-services/">Data Warehouse</a> and Snowflake. Once these changes are made, thorough testing needs to be performed between the reports using the legacy data warehouse and the equivalent reports using Snowflake.</p> </div>
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<p>– Compare report data<br />– Compare report layout<br />– Compare report performance<br />– Stress test reports in the new environments by simulating concurrent user loads<br />– Compare security</p> </div>
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<p>Datagaps BI Validator is a no-code <a href="https://www.datagaps.com/bi-testing-tools/bi-validator/">BI Testing Tool</a> that can help automate all these tests for the supported BI tools.</p> </div>
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<p>Try BI Validator free for 14 days for your Snowflake BI testing needs – <a href="https://www.datagaps.com/bi-testing-tools/bi-validator-download/">Download Now</a></p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/snowflake-data-migration-testing/">Data Validation for Snowflake</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Automated Cloud Data Testing | ETL, BI & BigData</a>.</p>
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<title>Datagaps Attains Snowflake Select Technology Partner Status</title>
<link>https://www.datagaps.com/blog/datagaps-attains-snowflake-select-technology-partner-status/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Tue, 08 Feb 2022 19:52:46 +0000</pubDate>
<category><![CDATA[BI Testing]]></category>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Dataflow]]></category>
<category><![CDATA[ETL Testing]]></category>
<category><![CDATA[Snowflake]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=3955</guid>
<description><![CDATA[<p>Datagaps Inc. announces a new partnership with Snowflake, the Data Cloud company. Datagaps attains Snowflake Select Technology Partner in the space of Data and Business Intelligence Validation to help organizations validate their migration to Snowflake’s Data Cloud.</p>
<p>The post <a href="https://www.datagaps.com/blog/datagaps-attains-snowflake-select-technology-partner-status/">Datagaps Attains Snowflake Select Technology Partner Status</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Automated Cloud Data Testing | ETL, BI & BigData</a>.</p>
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<p><a title="Datagaps" href="https://www.datagaps.com/" target="_blank" rel="noopener">Datagaps Inc.</a> announces a new partnership with <a href="https://www.snowflake.com/en/">Snowflake</a>, the Data Cloud company. Datagaps attains Snowflake Select Technology Partner in the space of Data and Business Intelligence Validation to help organizations validate their migration to Snowflake’s Data Cloud.</p> </div>
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<p>Snowflake delivers the Data Cloud, where thousands of organizations have seamless access to explore, share, and unlock the true value of their data.</p> </div>
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<p>Datagaps as a Select Technology Partner helps organizations validate their legacy migration to Snowflake’s platform. Datagaps ETL Validation helps ensure that the ETL processes are running correctly when the new data structures are pointed to. The Data Migration Wizard provides a quick method for validating that hundreds of legacy tables moved to the cloud correctly. Finally, as the BI applications need to be redirected to the cloud, BI Validator includes regression testing, performance testing, stress testing, and old vs new GUI comparisons to test the BI migration.</p> </div>
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<p class="et_pb_module_header">Naren <br />Yalamanchilli</p> </div>
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<p><em><i>Snowflake’s Platform</i><i> presents key advantages over traditional on-premises data solutions. However, migrating to Snowflake can requires careful planning and execution. Datagaps helps simplify this process by automating the data migration testing and analytics regression testing. Using our validation, customers have successfully validated billions of records and thousands of reports. We are looking forward to helping more Snowflake customers with this partnership</i></em></p> </div>
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<p>Moving data freely from one solution to another, and then to all your business users for truly democratized analytics, is not only vital to your organization but also one of your biggest challenges. Snowflake’s platform natively connects to dozens of other solutions, turning data into an asset you can easily derive valuable insights from without the cost, risk, and headache of trying to connect disparate, legacy systems.</p> </div>
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<p>With data today being the core of every business, it is essential that enterprises work with the right technology experts to get their outputs and analytics right. Datagaps data validation solutions, be it ETL Validator, BI Validator, or the DataOps Suite- (A complete suite for data validation and data quality with test data manager), have been very effective in achieving set data needs with all our customers.</p> </div>
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<p>We have seen a significant increase in inquiries regarding our ability to help validate the data migration process to Snowflake. People have turned to us to help validate the data migration, new vs. old ETL/ELT process data validation and on the backend, validate the data analytics old vs. new now pointing to Snowflake. We are excited about the opportunity to work with Snowflake’s clients and are thrilled to have achieved Select Partner Status with Snowflake.</p> </div>
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<p class="et_pb_module_header"><strong>David Small- VP Sales and Marketing, Datagaps Inc.</strong></p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/datagaps-attains-snowflake-select-technology-partner-status/">Datagaps Attains Snowflake Select Technology Partner Status</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Automated Cloud Data Testing | ETL, BI & BigData</a>.</p>
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<title>Datagaps Data Validation and Migration To Snowflake</title>
<link>https://www.datagaps.com/blog/data-validation-in-snowflake-data-migration/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Wed, 19 Jan 2022 12:31:22 +0000</pubDate>
<category><![CDATA[Snowflake]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=7286</guid>
<description><![CDATA[<p>This article describes ways in which we can test the data movement between Snowflake and on-premises data and between instances of Snowflake itself. We will also provide some benchmarks for doing comparison testing for large volumes of data moved into Snowflake.</p>
<p>The post <a href="https://www.datagaps.com/blog/data-validation-in-snowflake-data-migration/">Datagaps Data Validation and Migration To Snowflake</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Automated Cloud Data Testing | ETL, BI & BigData</a>.</p>
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<h2 class="elementor-heading-title elementor-size-default">Data validation in snowflake</h2> </div>
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This article discusses why and how to use both together, and dives into the challenges of Bulk <a href="https://www.datagaps.com/cloud-data-test-automation/snowflake/"> Data Migration to Snowflake.</a> </div>
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<h5 class="elementor-heading-title elementor-size-default">Why and How?</h5> </div>
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People are rapidly adopting cloud architectures for Data Warehouses and Machine Learning projects due to the economies of scale in the cloud. One obstacle in achieving rapid success is the data and data types inconsistencies between on-premise structures and the modern data stacks in the cloud. </div>
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When you migrate vast amounts of data to the cloud, the opportunity to introduce mistakes is multiplied due to these reasons and others. The earlier you catch the issues, the less costly it is to resolve the discrepancies. </div>
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<strong>This is where <a href="https://www.datagaps.com/">Datagaps</a> come in to play.</strong> </div>
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<p><a href="https://www.datagaps.com/wp-content/uploads/2022/01/database-and-Snowflake.svg"><br /><img loading="lazy" decoding="async" class="attachment-thumbnail size-thumbnail wp-image-6747" src="https://www.datagaps.com/wp-content/uploads/database-and-Snowflake.svg" alt="database-and-Snowflake" width="129" height="150" /> </a><br />We provide the ability to test millions & even billions of records between source and targets structures of different types such as your on premises database and Snowflake.</p> </div>
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This article describes ways in which we can test the data movement between Snowflake and on-premises data and between instances of Snowflake itself. We will also provide some benchmarks for doing comparison testing for large volumes of data moved into Snowflake. Another benefit of the Datagaps approach is, as additional data is moved into the Snowflake structure from other sources, Datagaps provides the ability to monitor the quality of your data structures by continually providing an up to date scoring so that you can determine when data is becoming corrupted. </div>
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<h2 class="elementor-heading-title elementor-size-default">Benefits of using Datagaps to test data movement into Snowflake</h2> </div>
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<p>We recently sat down with one of our clients that uses our DataFlow product for testing the data migration from on-Prem SQL Server to Snowflake in the cloud running in <a href="https://aws.amazon.com/">AWS.</a></p> </div>
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<img loading="lazy" decoding="async" width="464" height="53" src="https://www.datagaps.com/wp-content/uploads/Datagaps-Snowflake_1.svg" class="attachment-full size-full wp-image-6812" alt="Datagaps-Snowflake" /> </div>
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<h5 class="elementor-heading-title elementor-size-default">Implementation</h5> </div>
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<p>They started the initial migration by performing bulk loads from 400 SQL Server tables to Snowflake with minimal transformations. This was stage 0, where they could perform source and target data comparison for over 500 million rows of data per table. Making use of the Data Migration wizard, the client was able to generate comparison tests for 400 tables in just a few hours. Even though there were few changes at this stage, they still encountered errors that were surfaced by our<br />DataFlow product.</p> </div>
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<strong>Examples:</strong> Issues include numeric precision differences, null value inconsistencies, truncation issues and character interpolations. </div>
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<img loading="lazy" decoding="async" width="522" height="174" src="https://www.datagaps.com/wp-content/uploads/Datagaps-Snowflake.svg" class="attachment-full size-full wp-image-6814" alt="Datagaps-Snowflake" /> </div>
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Next, they began to perform incremental new data migrations where they continued to find similar issues that had to be corrected. As this continued, they wanted to transition from this incremental new data migration from SQL Server to loading the new data directly into Snowflake to reap the benefits stated earlier. To accomplish this, their initial ETL processes needed to be migrated to an ELT process aimed at Snowflake. </div>
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<p>DataFlow was used once again to check the accuracy between the two systems once the new processes were in place. The validations exposed issues in the new ELT process through several iterations until the transformation were in sync. After a short period of testing, they could cut over to the new system and deprecate the SQL server environment. Now DataFlow continued to validate the incremental data as it was moved into Snowflake, finding issues earlier in the cycle where they are less costly to fix in time and lost credibility.</p> </div>
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<h5 class="elementor-heading-title elementor-size-default">How it makes a difference?</h5> </div>
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Many of our clients take this one step further by testing their BI tools against the old warehouse and the new Snowflake implementation. They do regression, performance, and stress testing using our BI Validator tool to compare the old with the new. They can find differences in the look and feel in the output generation of the reports and dashboards. Differences are exposed between the report queries when compared to a database query. Often this is the last task necessary to validate the migration process. </div>
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Making use of the inbuilt cluster integration with AWS EMR in the DataOps suite, the client was able to automatically scale the EMR cluster on demand to over 30 nodes depending on the data volumes being compared and scale down once the testing has been completed. This capability helped reduce the cost of the testing while still achieving high performance when comparing tables of size <strong>500 Million Records</strong> </div>
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<h3 class="elementor-heading-title elementor-size-default">Amount of data tested increased from manual testing of <strong>10,000 sample records</strong> to complete testing of <strong>500 M</strong>.</h3> </div>
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<img loading="lazy" decoding="async" width="595" height="96" src="https://www.datagaps.com/wp-content/uploads/Datagaps-Snowflake_Banner.svg" class="attachment-full size-full wp-image-6856" alt="Datagaps-Snowflake_Banner" /> </div>
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<p>In conclusion, the goals of the migration project of agility, cost savings and performance improvements were achieved.</p><p>They also realized these benefits months earlier as a result of the improvement in the migration process due to the impact of the DataFlow products contribution in an estimated 50% test cycle reduction.</p><p>One of our clients reports comparing a file against a <strong>Snowflake</strong> instance with</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/data-validation-in-snowflake-data-migration/">Datagaps Data Validation and Migration To Snowflake</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Automated Cloud Data Testing | ETL, BI & BigData</a>.</p>
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