The following diagram in this ETL testing tutorial gives you the ROAD MAP of the ETL Testing process flow and various ETL testing concepts: ETL testing is done to ensure that the data that has been loaded from a source to the destination after business transformation is accurate. Equivalence Class Partitioning (ECP) bugs. The source and target databases, mappings, sessions and the system possibly have performance bottlenecks. Source QuerySELECT cust_id, fst_name, lst_name, fst_name||,||lst_name, DOB FROM Customer, Target QuerySELECT integration_id, first_name, Last_name, full_name, date_of_birth FROM Customer_dim. ETL is commonly associated with Data Warehousing projects but in reality any form of bulk data movement from a source to a target can be considered ETL. However, performing 100% data validation is a challenge when large volumes of data is involved. Incremental testing verifies that the inserts and updates are getting processed as expected during incremental ETL process. ETL stands for Extract-Transform-Load and it is a process of how data is loaded from the source system to the data warehouse. For example, there is a retail store which has different departments like sales, marketing, logistics etc. Review the requirement and design for calculating the interest. This data can then be leveraged for Data Quality & Interpretation, Data Mining, Predictive Analysis, and Reporting. Review the source to target mapping design document to understand the transformation design. In this Data Warehouse Testing tutorial, you will learn: Data Warehouse Testing is a testing method in which the data inside a data warehouse is tested for integrity, reliability, accuracy and consistency in order to comply with the companys data framework. However, source data keeps changing and new data quality issues may be discovered even after the ETL is being used in production. Compare table and column metadata across environments to ensure that changes have been migrated appropriately. The device is not responding to the application. monthly). Verifies that there are no redundant tables and database is optimally normalized. Failure to understand business requirements or employees are unclear of the business needs. These datas will be used for Reporting, Analysis, Data mining, Data quality and Interpretation, Predictive Analysis. All Rights Reserved. 
The purpose of Data Completeness tests are to verify that all the expected data is loaded in target from the source. Cleaning does the omission in the data as well as identifying and fixing the errors. 
Instances of fields containing values not found in the valid set represent a quality gap that can impact processing. Date values are using many areas in ETL development for. The huge volume of historical data may cause memory issues in the system. However, the ETL Testing process can be broken down into 8 broad steps that you can refer to while performing Testing: The first and foremost step in ETL Testing is to know and capture the business requirement by designing the data models, business flows, schematic lane diagrams, and reports. You can contribute any number of in-depth posts on all things data. Verify that the unique key and foreign key columns are indexed as per the requirement. Example 1: A column was defined as NOT NULL but it can be optional as per the design.Example 2: Foreign key constraints were not defined on the database table resulting in orphan records in the child table. There are several challenges in ETL testing: Test Triangle offer following testing services: Test Triangle is an emerging IT service provider specializing in
ETL Validator comes withData Profile Test Case, Component Test Case and Query Compare Test Casefor automating the comparison of source and target data. To start with, setup of test data for updates and inserts is a key for testing Incremental ETL. Every Testing team has different requirements, and thus it is important to choose the ETL Testing tool to avoid future bottlenecks carefully. Instances of fields containing values violating the validation rules defined represent a quality gap that can impact ETL processing. When running in Full mode, the ETL process truncates the target tables and reloads all (or most) of the data from the source systems. Data is often transformed which might require complex SQL queries for comparing the data. Type 2 SCD is designed to create a new record whenever there is a change to a set of columns. databases, flat files). The data type and length for a particular attribute may vary in files or tables through the semantic definition is the same. Benchmarking capability allows the user to automatically compare the latest data in the target table with a previous copy to identify the differences. Example: A new column added to the SALES fact table was not migrated from the Development to the Test environment resulting in ETL failures. Alternatively, all the records that got updated in the last few days in the source and target can be compared based on the incremental ETL run frequency. One such tool is Informatica. Example: Write a source query that matches the data in the target table after transformation.Source Query, SELECT fst_name||,||lst_name FROM Customer where updated_dt>sysdate-7Target QuerySELECT full_name FROM Customer_dim where updated_dt>sysdate-7. Column or attribute level data profiling is an effective tool to compare source and target data without actually comparing the entire data. Example 1: The length of a comments column in the source database was increased but the ETL development team was not notified. Target table loading from stage file or table after applying a transformation. The next step involves executing the created test cases on the QA (Question-Answer) environment to identify the types of bugs or defects encountered during Testing. The purpose of Incremental ETL testing is to verify that updates on the sources are getting loaded into the target system properly. Check for any rejected records. Example: The naming standard for Fact tables is to end with an _F but some of the fact tables names end with _FACT. Compare the transformed data in the target table with the expected values for the test data. Compare data in the target table with the data in the baselined table to identify differences. The goal of ETL Regression testing is to verify that the ETL is producing the same output for a given input before and after the change. Large enterprises often have a need to move application data from one source to another for data integration or data migration purposes. Verify that the length of database columns are as per the data model design specifications. However, the denormalized values can get stale if the ETL process is not designed to update them based on changes in the source data. Execute the ETL before the change and make a copy of the target table. Cleansing of data :After the data is extracted, it will move into the next phase, of cleaning and conforming of data.
Performance Testing in ETL is a testing technique to ensure that an ETL system can handle load of multiple users and transactions. The primary goal of ETL Performance Testing is to optimize and improve session performance by identification and elimination of performance bottlenecks. The solutions provided are consistent and work with different BI tools as well. Its Architecture: Data Lake Tutorial, 20 BEST SIEM Tools List & Top Software Solutions (Jul 2022). Many database fields can contain a range of values that cannot be enumerated. The goal of these checks is to identify orphan records in the child entity with a foreign key to the parent entity. Possessing In case you want to set up an ETL procedure, then Hevo Data is the right choice for you! Executing incremental ETL. Compare your output with data in the target table. Source data is denormalized in the ETL so that the report performance can be improved. It is also a key requirement for data migration projects. Automate ETL regression testing using ETL ValidatorETL Validator comes with aBaseline and Compare Wizardwhich can be used to generate test cases for automatically baselining your target table data and comparing them with the new data. This helps ensure that the QA and development teams are aware of the changes to table metadata in both Source and Target systems.
), Difference Between Database Testing and ETL Testing.
Testing data transformation is done as in many cases it cannot be achieved by writing one source.
Compare count of records of the primary source table and target table. Data is transformed during the ETL process so that it can be consumed by applications on the target system. ETL Validator comes withMetadata Compare Wizardfor automatically capturing and comparing Table Metadata. What is an ETL Tester: 4 Best Tips And Practices, 5 Best ETL Automation Testing Tools for 2022, The Best 101 Guide to Zephyr Jira Testing. Example: Write a source query that matches the data in the target table after transformation.
between source and target systems. Identify the problem and provide solutions for potential issues, Approve requirements and design specifications, Writing SQL queries3 for various scenarios like count test, Without any data loss and truncation projected data should be loaded into the data warehouse, Ensure that ETL application appropriately rejects and replaces with default values and reports invalid data, Need to ensure that the data loaded in data warehouse within prescribed and expected time frames to confirm scalability and performance, All methods should have appropriate unit tests regardless of visibility, To measure their effectiveness all unit tests should use appropriate coverage techniques. The objective of ETL testing is to assure that the data that has been loaded from a source to destination after business transformation is accurate. Review the requirement for calculating the interest. However, a DOB in the future, or more than 100 years in the past are probably invalid. Prepare test data in the source systems to reflect different transformation scenarios. Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Frequent changes in the requirement of the customers cause re-iteration of test cases and execution. It is similar to comparing the checksum of your source and target data. Find out the difference between ETL vs ELT here. Also, the date of birth of the child is should not be greater than that of their parents.
Example: Date of birth (DOB). Using the component test case the data in the OBIEE report can be compared with the data from the source and target databases thus identifying issues in the ETL process as well as the OBIEE report.
The product validation testing ensures that the information present in the database is correct and reliable. Reports are prepared based on the bugs and test cases and are uploaded into the Defect Management Systems. ETL testing is very much dependent on the availability of test data with different test scenarios. When setting up a data warehouse for the first time, after the data gets loaded. The raw data is the records of the daily transaction of an organization such as interactions with customers, administration of finance, and management of employee and so on. Often changes to source and target system metadata changes are not communicated to the QA and Development teams resulting in ETL and Application failures. An executive report shows the number of Cases by Case type in OBIEE.
It also explains the potential of Testing Tools.
Compare table metadata across environments to ensure that metadata changes have been migrated properly to the test and production environments. ETL Validator comes withBaseline & Compare WizardandData Rules test planfor automatically capturing and comparing Table Metadata. Here are the different phases involved in the ETL Testing process: The primary responsibilities of an ETL Tester can be classified into one of the following three categories: Here are a few pivotal responsibilities of an ETL Tester: Here are a few situations where ETL Testing can come in handy: ETL Testing is the process that is designed to verify and validate the ETL process in order to reduce data redundancy and information loss.
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SELECT count(1) tgt_count FROM customer_dim. Is a new record created every time there is a change to the SCD key columns as expected? End-to-end testing of the enterprise warehouse system, Lack of comprehensive coverage due to large data volume. After logging all the defects onto Defect Management Systems (usually JIRA), they are assigned to particular stakeholders for defect fixing. Source data type and target data type should be same, Length of data types in both source and target should be equal, Verify that data field types and formats are specified, Source data type length should not less than the target data type length. Automating the data quality checks in the source and target system is an important aspect of ETL execution and testing. It ensures that all data is loaded into the target table. Load data from a source of your choice to your desired destination in real-time using Hevo. Copyright 2022 Test Triangle. February 22nd, 2022 These upgrades create compatibility issues and systematic testing approach is required to check system compatibility with the new versions. Metadata Testing involves matching schema, data types, length, indexes, constraints, etc. Performance of the ETL process is one of the key issues in any ETL project. If minus query returns of rows and count intersect is less than source count or target table then we can consider as duplicate rows are existed. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. Compare the results of the transformed test data in the target table with the expected values. Validate the name of columns in the table against mapping doc. Validates the source and target table structure with the mapping doc. Data Quality Tests includes syntax and reference tests. Syntax Tests: It will report dirty data, based on invalid characters, character pattern, incorrect upper or lower case order etc. Analysts must try to reproduce the defect and log them with proper comments and screenshots. ETL Validator also comes withMetadata Compare Wizardthat can be used to track changes to Table metadata over a period of time.
are tested. Organizing test cases into test plans (or test suites) and executing them automatically as and when needed can reduce the time and effort needed to perform the regression testing.
Define data rules to verify that the data conform to the domain values.
As part of this testing it is important to identify the key measures or data values that can be compared across the source, target and consuming application. Read along to find out about this interesting process. These differences can then be compared with the source data changes for validation. Due to changes in requirements by the customer, a tester might need to re-create/modify mapping documents and SQL scripts, which leads to a slow process. The tester is tasked with regression testing the ETL. Verify mapping doc whether corresponding ETL information is provided or not. The metadata testing is conducted to check the data type, data length, and index. Using this approach any changes to the target data can be identified. Verify that the columns that cannot be null have the NOT NULL constraint. An ETL process automatically extracts the data from sources by using configurations and connectors and then transforms the data by applying calculations like filter, aggregation, ranking, business transformation, etc. The Customer address shown in the Customer Dim was good when a Full ETL was run but as the Customer Address changes come in during the Incremental ETL, the data in the Customer Dim became stale. To support your business decision, the data in your production systems has to be in the correct order. The raw data would refer to the records of the daily transaction of an organization like interactions with the administration of finance, customers, and management of employees, among others. Various. Conforming means resolving the conflicts between those datas that is incompatible, so that they can be used in an enterprise data warehouse. The ETL tests must be executed as per business requirements. Track changes to Table metadata over a period of time. The ETL testing is conducted to identify and mitigate the issues in data collection, transformation and storage. Example: Compare Country Codes between development, test and production environments. When the data volumes were low in the target table, it performed well but when the data volumes increased, the updated slowed down the incremental ETL tremendously. Hevo Data, a No-code Data Pipeline helps to load data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process. There are two approaches for testing transformations white box testing and blackbox testing. Example: Data Model column data type is NUMBER but the database column data type is STRING (or VARCHAR). ETL Testing is derived from the original ETL process. To know the row creation dateIdentify active records as per the ETL development perspective, To validate the complete data set in source and target table minus a query in the best solution. Unnecessary columns should be deleted before loading into the staging area. In this type of Testing, SQL queries are run to validate business transformations and it also checks whether data is loaded into the target destination with the correct transformations. The goal of ETL integration testing is to perform an end-to-end testing of the data in the ETL process and the consuming application. The different phases of ETL testing process is as follows. It may not be practical to perform an end-to-end transformation testing in such cases given the time and resource constraints. Once the data is transformed and loaded into the target by the ETL process, it is consumed by another application or process in the target system. All Rights Reserved. These approaches to ETL testing are time-consuming, error-prone and seldom provide completetest coverage. Verify if data is missing in columns where required. Execute ETL process to load the test data into the target. Example: Values in the country_code column should have a valid country code from a Country Code domain.select distinct country_code from address minus select country_code from country. Integration testing of the ETL process and the related applications involves the following steps: Example: Lets consider a data warehouse scenario for Case Management analytics using OBIEE as the BI tool. Often development environments do not have enough source data for performance testing of the ETL process. Automate data transformation testing using ETL ValidatorETL Validator comes withComponent Test Casewhich can be used to test transformations using the White Box approach or the Black Box approach. When a source record is updated, the incremental ETL should be able to lookup for the existing record in the target table and update it. Identify the Problem and offer solutions for potential issues. This article focuses on providing a comprehensive guide on ETL Testing.
Real-time data may impact the reconciliation process between data sources and target destinations. strong experience in different industry verticals such as Banking & Build aggregates Creating an aggregate is summarizing and storing data which is available in, Identifying data sources and requirements, Implement business logics and dimensional Modelling. Example: Business requirement says that a combination of First Name, Last Name, Middle Name and Data of Birth should be unique.Sample query to identify duplicatesSELECT fst_name, lst_name, mid_name, date_of_birth, count(1) FROM Customer GROUP BY fst_name, lst_name, mid_name HAVING count(1)>1. These tests are essential when testing large amounts of data. The ETL process consists of 3 main steps: In order to understand the ETL process in a more detailed fashion, click here. it checks the loss/truncation of the data in the target systems. White box testing is a testing technique, that examines the program structure and derives test data from the program logic / code. Its important to understand business requirements so that the tester can be aware of what is being tested. Metadata testing includes testing of data type check, data length check and index/constraint check. Here, you need to make sure that the count of records loaded within the target is matching with the expected count.
Example: Business requirement says that a combination of First Name, Last Name, Middle Name and Data of Birth should be unique. ETL Validator comes withComponent Test Casethe supports comparing an OBIEE report (logical query) with the database queries from the source and target. Automating ETL testing can also eliminate any human errors while performing manual checks. In case there are any suspected issues with the performance of ETL processes. Download your 14 day free trial now. Verify the null values, where Not Null is specified for a specific column. ETL Validator comes withData Rules Test Plan and Foreign Key Test Planfor automating the data quality testing. The solution is to use a datawarehouse to store information from different sources in a uniform structure using ETL. The sales department have stored it by customers name, while marketing department by customer id.
It is essential to validate that existing data is not jeopardized with the system upgrades. Hevo Data Inc. 2022. While performing ETL testing, two documents that will always be used by an ETL tester are, Verifies whether the data transformed is as per expectation, Key responsibilities of an ETL tester are segregated into three categories, Some of the responsibilities of an ETL tester are. It ensures that the constraints defined for each table are correct. This includes invalid characters, patterns, precisions, nulls, numbers from the source, and the invalid data is reported. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs. Changes to MetadataTrack changes to table metadata in the Source and Target environments. With the introduction of Cloud technologies, many organizations are trying to migrate their data from Legacy source systems to Cloud environments by using ETL Tools. Boundary Value Analysis (BVA) related bug, Equivalence Class Partitioning (ECP) related bug, Verifies whether data is moved as expected, The primary goal is to check if the data is following the rules/ standards defined in the Data Model, Verifies whether counts in the source and target are matching, Verify that there are no orphan records and foreign-primary key relations are maintained, Verifies that the foreign primary key relations are preserved during the ETL, Verifies that there are no redundant tables and database is optimally normalized, Verify if data is missing in columns where required, Transform data to DW (Data Warehouse) format, Build keys A key is one or more data attributes that uniquely identify an entity. ETL Testing comes into play when the whole ETL process needs to get validated and verified in order to prevent data loss and data redundancy. Here, you need to check if there is any duplicate data present in the target system. Target Table Loading from Stage Table or File after Applying a Transformation. It explained the process of Testing, its types, and some of its challenges. The data migration testing is conducted to identify the inconsistencies in data migration in the early data migration testing. In regulated industries such as finance and pharmaceutical, 100% data validation might be a compliance requirement. The report will help the stakeholders to understand the bug and the result of the Testing process in order to maintain the proper delivery threshold. Denormalization of data is quite common in a data warehouse environment. This check is important from a regression testing standpoint. Write for Hevo. Similar to other Testing Process, ETL also go through different phases. Verify that the table and column data type definitions are as per the data model design specifications. Needs to validate the unique key, primary key and any other column should be unique as per the business requirements are having any duplicate rows. Data quality testing includes number check, date check, precision check, data check , null check etc. In this technique, the datatype, index, length, constraints, etc.
Although there are slight variations in the type of tests that need to be executed for each project, below are the most common types of tests that need to be done for ETL Testing. This article gave a comprehensive overview of ETL Testing. From a pure regression testing standpoint it might be sufficient to baseline the data in the target table or flat file and compare it with the actual result in such cases. It checks if the data is following the rules/ standards defined in the Data Model. Once the developer fixes the bug, the bug is tested in the same environment again to ensure there are no traces of the bug is left. Hence, to get better performance, scalability, fault-tolerant, and recovery systems, organizations migrate to Cloud technologies like Amazon Web Services, Google Cloud Platform, Microsoft Azure, Private Clouds, and many more. Implement the logic using your favourite programming language.
However, during testing when the number of cases were compared between the source, target (data warehouse) and OBIEE report, it was found that each of them showed different values. ETL Testing is different from application testing because it requires a data centric testing approach. To verify that all the expected data is loaded in target from the source, data completeness testing is done. Some of the tests that can be run are compare and validate counts, aggregates and actual data between the source and target for columns with simple transformation or no transformation. Are the old records end dated appropriately? If an ETL process does a full refresh of the dimension tables while the fact table is not refreshed, the surrogate foreign keys in the fact table are not valid anymore. Verify the null values, where Not Null specified for a specific column.
Organizations may have Legacy data sources like RDBMS, DW (Data Warehouse), etc. Check data should not be truncated in the column of target tables, Compares unique values of key fields between data loaded to WH and source data, Data that is misspelled or inaccurately recorded, Number check: Need to number check and validate it, Date Check: They have to follow date format and it should be same across all records, Needs to validate the unique key, primary key and any other column should be unique as per the business requirements are having any duplicate rows, Check if any duplicate values exist in any column which is extracting from multiple columns in source and combining into one column, As per the client requirements, needs to be ensure that no duplicates in combination of multiple columns within target only, Identify active records as per the ETL development perspective, Identify active records as per the business requirements perspective.