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    What is ETL testing?

    What is ETL testing?

    ETL stands for Extract, Transform, Load, and is the process of loading or migrating data from one or more sources to a destination source, data warehouse, or any other unified data repository.


    The first step is extracting data from the source (or sources)


    The second step is preparing the data or transforming it to match the format or structure of the destination database technology.


    The final step, loading the extracted data into the destination data warehouse.

    • ETL testing refers to the process of validating, verifying, and qualifying data. ETL testing is done to ensure that the transfer of data from heterogeneous sources to the central data warehouse occurs with strict adherence to transformation rules and is in compliance with all validity checks. . ETL testing is applied to data warehouse systems and used to obtain relevant information for analytics and business intelligence.
    • ETL testing is also done to ensure that the data that has been loaded from a source to the destination after business transformation is accurate. It also involves the verification of data at various middle stages that are being used between source and destination.
    • ETL testing is made to confirm that the data we have extracted, transformed, and loaded has been extracted completely, transferred properly, and loaded into the new system in the correct format.
    • ETL Testing can also help identify and prevent issues with data quality during the ETL process, such as duplicate data or data loss. ETL Testing also confirms that the ETL process itself is running smoothly and that there are no issues.
    • We (DataQ) want to make sure that the ETL process doesn’t suffer from performance issues that might impact the performance of either the source or destination systems and also performs testing in less time.
    ETL vs. ELT

    ETL vs. ELT

    Parameters ETL ELT
    Action Data testing is done when data is transformed at the staging server and then after testing transferred to Datawarehouse. Data remains in the DB of the Data Warehouse. Data testing is done in the data warehouse after transformation.
    • Compute-intensive testing done after Transformations.
    • Testing is required for a small amount of data.
    Testing is required for a large volume of data
    Transformation Transformations (Data Testing ) are done in the ETL server/staging area. Transformations (Data Testing) are performed in the target system
    Time-measure Data is first loaded into staging and then tested and then loaded into the target system. Data Testing is done after loading into target systems. Data is loaded into the target system only once and testing is done after this. Faster.
    Support for Data warehouse ETL model used for on-premises, relational and structured data. Data Testing is also supported for data warehouses Used in scalable cloud infrastructure which supports structured, unstructured data sources. Data Testing is also capable of handling structured and unstructured data.
    Data Lake Support Data testing can’t be done as it is not supported. Allows use of Data lake with unstructured data. Data testing should be capable of handling Data Lakes also..
    Complexity The ETL process loads only the important data, as identified at design time. Data Testing can also be designed for better testing. This process involves development from the output-backward and loading only relevant data. Data testing can be done for these relevant data.
    Lookups In the ETL process, both facts and dimensions need to be available in the staging area. Data testing should have the capability for lookup. All data will be available because Extract and load occur in one single action. Data testing can be done at multiple stages.
    Aggregations Complexity increases with the additional amount of data in the dataset. DataQ is capable of handling data involved in testing. The power of the target platform can process a significant amount of data quickly. DataQ is a faster and more suitable way for data testing
    Calculations Overwrites existing column or needs to append the dataset and push to the target platform. For Data testing, need to understand the requirements to implement. Easily add the calculated column to the existing table. Requirements are easy to understand and implement data testing.
    Maturity The process is used for over two decades. It is well documented and best practices easily available. Data Q has a good capability to handle data testing for ETL as well. Relatively new concept and complex to implement. Data testing using DataQ is easier.
    Support for Unstructured Data Mostly supports relational data and data testing is easier for relational data. Support for unstructured data is readily available. Data Testing requires more effort in comparison to unstructured data.
    Eight stages of the ETL Testing Process


    Identify business requirements

    Define the complete business requirement of the project. This involves defining the data sources, the data destination, the technology involved, the level of reporting needed, etc. Create an exhaustive business requirement document.

    Validate data sources

    Perform a thorough data count check of the source. This will require cleaning duplicate data and then performing a data count check for tables, rows, and columns. This information will help you validate the authenticity of the migration.

    Design test cases

    Test the data on the destination DB warehouse to ensure the data type meets the format and specifications of the data model. This step also requires you to create mapping scenarios, SQL scripts, and transformation rules.

    Extract data from source systems

    Extract the data from the source DB. Perform validation checks to ensure all the data, in its proper format and structure, has been extracted. It is important to identify any anomalies or bugs during this stage before transforming or loading.

    Apply transformation logic

    Transform the data as per the defined transformation rule to match the schema of the destination database warehouse. Perform validation and tests on the staging server to ensure complete data transformation has occurred, and data mapping is as planned.

    Load data into target warehouse

    Load the extracted and transformed data from the staging server to the destination DB warehouse. Perform ETL testing to ensure the data is authentic, matches the record count, and is operational.

    Summary report

    fixed errors, etc. This document will help stakeholders understand the results of the ETL system.

    Test closure

    Close the ETL process with all documents and reporting in place.

    The 6 Key Steps


    Metadata Testing

    Metadata testing includes data type check, data length check, and index/constraint check. It validates that the metadata is congruent with the data model and the specs of the application.

    Data Completeness Testing

    Data completeness testing is performed to ensure all data from the source has migrated to the destination warehouse. Tests performed are to compare and validate data counts, data aggregates, and actual data comparison between source and destination.

    Data Quality Testing

    Data quality testing uses data profiling to identify issues in the quality of the data, and the ETL system is designed (or automated) to fix these issues. The purpose of data quality testing is to ensure the complete authenticity of the migrated data.

    Data Transformation Testing

    Data transformation testing is performed to ensure that the data from the source is correctly transformed to suit the schema of the destination DB warehouse. This involves either examining the program structure to develop test data and review the transformation logic or examining app functionality and analyzing the transformation logic by mapping the design document to the test data.

    ETL Regression Testing

    This test validates that the ETL system delivers the same output for a given input before and after the change.

    Incremental ETL Testing

    This test validates that the source updates are getting loaded into the destination system correctly. This includes checking for duplicates at the target (when the source is updated), data value comparison, and denormalization checks.

    ETL Integration Testing

    This involves the complete end-to-end testing of data in the ETL system. ETL integration testing is done by setting up test data in the source system, executing the ETL process to load the data into the destination system, processing the data at the destination, comparing results, and validating it at the destination application.

    ETL Performance Testing

    This testing process involves the complete testing of all steps in the ETL process with data sets. It involves setting up test data, running the ETL system, loading data into the target system, and analyzing all steps involved during this process.

    ETL Testing Challenges

    ETL Testing Challenges

    One of the biggest challenges with ETL testing is the volume of data. ETL systems often move huge volumes of data from heterogeneous data sources and require transformation to work on the target warehouse and application. This creates bottlenecks in operations, takes a lot of time, and is vulnerable to human errors. Some core challenges identified with ETL testing are:

    • Loss or corruption of data during the ETL move
    • Limited volumes of source data resulting in limited testing
    • Not planning business and app requirements properly
    • Loading duplicate data
    • Manual and slow testing methods
    • The use of manual and outdated ETL tools

    Most ETL challenges stem from using manual and outdated processes. Creating data maps, source tests, coding the ETL system, running transformation tests, etc., manually is time-consuming and opens room for human errors.

    These challenges can be easily overcome by using modern, AI-enabled ETL tools. Modern solutions for ETL testing provide an array of features like graphical interfaces and hot testing (on-the-fly testing) that allows developers to run ETL testing more efficiently and faster.

    Top Features Of An ETL Testing Tool


    Automation Enabled

    Automation drastically reduces ETL time and errors by automating core steps in the ETL process - code development, data mapping, transformation testing, etc.

    Graphical User Interface

    An ETL tool with a GUI makes development and testing processes faster and enables developers to modify and test on the fly.

    Complex Data Management

    Through modern and powerful tools like data connectors, content management, CI/CD integration, and sophisticated debugging tools.

    Secure and Compliant

    Data flowing through the ETL system can be sensitive and should be safe against online vulnerabilities. The tool should meet all safety compliance checks.

    ETL Testing With DataQ

    ETL Testing With DataQ

    DataQ is an intuitive and smart data quality automation tool. It delivers a feature-rich data migration tool with automation features for data migration, data observability, ETL testing, data reconciliation, and more.

    DataQ integrates with sources like Microsoft SQL Server, MongoDB, PostgreSQL, Oracle, Apache Hive, IBM DB2, SAP HANA, Teradata, MySQL, memSQL, Derby, MariaDB, and destination DB warehouses like Postgres, Snowflake, Big Query, Redshift, Cassandra, Hadoop, and Hive.

    DataQ supports both Multi-Cloud infrastructure and on-premise architecture.