ETL Testing, in simple terms, is the process of testing whether or not the data that you have Extracted, Transferred, and Loaded (ETL) into the desired destination has been done correctly and according to the business requirements. In this process, you first verify the data sources from where you have extracted the data and then verify the data transferred (i.e. cleaned and integrated). This ensures that it has been done accurately and efficiently. In the last step, you verify the data loaded in the Data Warehouse is in the correct format.
The entire process of data ETL is a little complex. Therefore, it is usually advised to use ETL Tools, customized to your business model. There are several ETL Tools available in the market such as Hevo Data, Xplenty, etc. These ETL Tools help in handling complex data, reduce Error Probability, and improve Business Intelligence & Rate of Investment (ROI).
Importance of ETL Testing
- Identify Problems at an Early Stage: With ETL Testing, you can identify problems (if any) with the source information at an early stage. This helps in identifying ambiguities or discrepancies in the data when crossed against the rules set by the company.
- Prevents Data Loss and Redundancy: ETL Testing is an effective method for authenticating and validating data. Hence, you can easily prevent Data Loss and Data Redundancy.
- Facilitates the Transfer of Bulk Data: ETL Testing is one of the important aspects when it comes to transferring Bulk Data from source to destination. It facilitates a seamless and effective way to transfer data in bulk.
Challenges of ETL Testing
Listed below are the challenges you might face while working with ETL Testing:
- Restricted Accessibility: You are not authorized to check source information i.e. there is restricted accessibility of source information. This can lead to a delay in the testing process.
- Improper Flow of Information: There is no proper flow or rules in your business information which makes it difficult to test all your ETL steps.
- Time-Consuming: ETL Testing of transformed columns is at times very complicated. Moreover, strategizing for the entire process of ETL Testing is time-consuming.
8 Stages of ETL Testing
The entire process of ETL Testing is broken down into 8 stages. Listed below are those 8 stages:
- Identify the Business Requirements: Before actually starting with ETL Testing, you need to have a clear understanding of your business requirements. Based on your business requirements, you’ll need to design a Data Model. In case you already have a Data Model, you’ll have to update it according to your business requirements. Furthermore, you should also have a clear picture of the data sources, target system, and the level of transformation required between them.
- Validate all the Data Sources: Once you have a clear picture of your business requirements, it is time to validate all the data sources you are working with. Perform a Data Count Check and also verify whether or not the column data type meets the requirements of the data model that you designed in the last stage.
- Design the Test Cases: In this stage, you need to design test cases to ensure seamless data transfer from source to destination. While designing these, consider the data in the target system for validating the Data Quality and performance of your Data Pipeline.
- Extract Data from the Data Sources: As simple as it sounds, in this step, you need to extract data from the source systems. Verify that the data sources are valid and the process of Data Extraction has been done as per the business requirements.
- Perform the Necessary Data Transformation: In this stage, you need to validate if the transformed data matches the schema of the target destination. Furthermore, validate the Data Threshold, Alignment, and Data Flow. This makes sure that the data type aligns with the mapping document for each of the columns in the destination table.
- Load Data into the Target Destination: Once the data has been transformed, load it into the target destination. Here, you need to ensure that invalid data (i.e. data that is not required) is rejected and the default data values are accepted. Experts also suggest performing a Record Count Check before and after loading the data.
- Document your Findings: Prepare an extensive report that includes bugs or issues you faced during the entire process and also the countermeasures you took to overcome or avoid those issues. This report provides an overall picture of the entire process to the decision-makers. It also helps them make necessary decisions for the future.
- Conclude: In this stage, conclude the testing that you just performed and proceed with the ETL (Extract, Transform, and Load) process for transferring your data in bulk.
Types of ETL Testing
ETL Testing plays a significant role in your business and helps make important decisions. There are majorly 4 types of ETL Testing and each of the types focuses on different parameters. Listed below are the 4 types of ETL Testing:
- Data-Centric Testing: It entirely revolves around testing the Data Quality and therefore helps in maximizing high-quality data. Its primary objective is to keep a check on the data transferred from source to destination. It also ensures that only valid and correct data is being transferred.
- Data Accuracy Testing: It ensures that the data transformed and loaded into the desired destination matches the schema of the Destination Table. This type of testing also helps identify errors that occurred due to improper mapping of columns, truncation of characters, etc.
- Business Testing: In simple words, this type of testing ensures that the ETL process is fulfilling the critical business requirements. Here, all the steps are evaluated against the business rules stated.
- Data Integrity Testing: It helps you check the count of “Unspecified” or “Unmatched” rows with respect to the foreign keys. This ensures that the data is accurate and reliable to use for Business Intelligence purposes.
This blog talks about ETL Testing in detail and provides you with some of the important aspects of ETL Testing. It also provides a comprehensive overview of different stages of ETL Testing. In case you want to load data from any data source such as Databases, SaaS Applications, etc., to the desired destination in real-time, then you should explore Hevo Data. Hevo Data’s No-code Data Pipeline simplifies the ETL process and enriches your data. You further transform your data into an analysis-ready form without writing a single line of code.