ETL Testing In Snowflake Using DataOps Suite

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Introduction and Overview of ETL Testing Snowflake

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. The sources may include Flat Files, Third-Party Applications, Databases, etc. ETL testing is necessary to ensure that data moving from external sources to the data warehouse is accurate at each point between the source and destination.

Purpose of ETL

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.

ETL Testing Process in Snowflake

The data will be migrated from one data warehouse to another cloud-based data warehouse 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. Request Demo

The various steps involved in ETL Testing are as follows:

Step 1: Extraction Of Data

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.

Data-Extraction-From-Customers-Table
DataOps Suite: Data Extraction From Customers Table

Step 2: Transformation Of Data

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.

The below screenshot depicts the Customer data that is being transformed.

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DataOps Suite: Data Transformation Using SQL Component

Once the data is transformed, data comparison can be performed to view the changes after transformation.

Comparison-Of-Data-Using-Data-Compare-Component
DataOps Suite: Comparison Of Data Using Data Compare Component

Further, the quality of data can be checked by using the Data Rules Component. ​​​​​​​​​​​​​​Data quality checks are done to find out the issues in the quality of data. The Data Profile Component can also be used to find out the data quality issues.

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.

Data-Quality-Check-Using-Data-Rules-Component
DataOps Suite: Data Quality Check Using Data Rules Component

​​​​​​​Data profiling is also done to check the quality of data.

Profiling-Data-Using-Data-Profile-Component
DataOps Suite: Profiling Data Using Data Profile Component

Step 3: Loading Of The Data

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 DB Sink component. This is the general testing process followed in the DataOps Suite tool. Request Demo

The below screenshot depicts the data loaded to the desired data source after the data transformations are done.

Data-Loading-Using-DB-Sink-Component
DataOps Suite: Data Loading Using DB Sink Component

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.

 

Read: Data Warehouse Testing Checklist

Conclusion

ETL testing 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 BI Validator.

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Established in the year 2010 with the mission of building trust in enterprise data & reports. Datagaps provides software for ETL Data Automation, Data Synchronization, Data Quality, Data Transformation, Test Data Generation, & BI Test Automation. An innovative company focused on providing the highest customer satisfaction. We are passionate about data-driven test automation. Our flagship solutions, ETL ValidatorDataFlow, and BI Validator are designed to help customers automate the testing of ETL, BI, Database, Data Lake, Flat File, & XML Data Sources. Our tools support Snowflake, Tableau, Amazon Redshift, Oracle Analytics, Salesforce, Microsoft Power BI, Azure Synapse, SAP BusinessObjects, IBM Cognos, etc., data warehousing projects, and BI platforms.  www.datagaps.com 

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