ETL Testing

Implement end to end automation for ETL Testing
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ETL is the process of Extracting (E) data from source systems, Transforming (T) the data based on business requirements, and then Loading (L) the transformed data into the destination database.

ETL processes form the core for data related projects like Data Warehouse, Data Migration, MDM, and Big Data. Every organization has thousands, if not hundreds of thousands of ETL processes running daily. These ETL jobs are processing financial data, marketing data, customer data, or other based on which business users make vital decisions.

Today, due to an exponential surge in the volume and type of data, specifically Big data. ETL has added challenges like

  • The number of ETL process has increased
  • The complexity of the ETL process has increased
  • Big data has resulted in an exponential increase in data volume

Therefore, it is critical to adopt a test-driven development (TDD) of ETL, which will enable organizations to ensure their data is correct as well as help businesses trust that data.

Below are the different types of projects where ETL is necessary.

Data Warehouse Testing-iCEDQ
Data Migration Testing-iCEDQ
Big Data Testing-iCEDQ

Application Testing vs. ETL Testing

Testing a Web, Desktop or Mobile Application refers to testing the behavior of the Graphical User Interface and associated functionality using a variety of scripting languages and tools.

In contrast, ETL processes run in the background and there is no Graphical User Interface available to test. Hence, ETL Testing requires the verification of input data and output data of an ETL process based on various types of rules.

What is ETL Testing?

ETL Testing is a methodology enabling a user to test an ETL process by validating and comparing source data to destination data using business rules or transformation rules . Thus, ETL testing is essentially a form of black-box testing without the bells and whistles of a full blown graphical user interface.

What to test in an ETL Process?

Data Transformation

The process of converting the data from one format or granularity to another is Data Transformation. ETL processes use filters, aggregators, lookup, and other transforms for effecting Data Transformations. .Testing and identifying data transformation issues by comparing and testing input and output data is the core activity constituting ETL testing.

Business Rules

In spite of a technically correct ETL transformation, devoid of any technical defects or issues, the likelihood of negating or violating business rules remains highly probable. This necessitates an extra step of business rules validations over and above the technical ETL testing. Thus, technical ETL testing is a part of the larger ETL testing umbrella and must be complemented by business rules testing for a comprehensive ETL test coverage.

Data Mapping

An ETL process is developed by mapping the source data attributes to the target attributes. This mapping of information to load the data from source to destination may result in data malformations, data corruption and defects. Thus, it is essential to test an ETL process for data mapping issues.

Data Quality

Amongst other things, data quality checks after an ETL process is completed includes checking that the ETL process is loading the data with correct formats, reference values, or not generating any duplicates is part of testing an ETL process.

Challenges of ETL Testing

  • An ETL process is a complete black box. There is no graphical user interface that someone can use for ETL testing. Therefore testing an ETL process is challenging as conventional testing tools like selenium are not useful.
  • Comparing large volumes of data manually by eyeballing is highly prone to error. A manual inspection process also forces the user test a smaller sample for ETL Testing. This can result in missing critical and essential ETL testing scenarios.
  • Testing data across heterogeneous data sources such as an on-prem database, flat file, and cloud data warehouse is never straight forward.
  • ETL transformations keep changing based on new business requirements. Hence, regression ETL testing becomes extremely complex and costly in the absence of automation testing.
  • ETL testing is absolutely dependent on valid source data and target data. Hence, identifying correct test data to cover all testing scenarios becomes a challenging task.
Challenges of ETL Testing-iCEDQ

Types of ETL Testing Techniques

Below are different scenarios on how to test an ETL process.

Scenario #1

An ETL process is loading customer data from a file into a table as is. The various tests a user can create are summarized below.

Source Data Validation

This ETL testing check allows the user to ensure that an incoming data or input data is valid. This type of check is also known as the Data Quality ETL testing rule. The test involves checking for nulls, formats, reference values, duplicates, etc. For example,

  • Verify that there are no null values in attribute “Name” in the file
  • The format of the date in the DOB column should be YYYY-MM-DD

Source to Target ETL Testing

Comparing the source (input) data and the target (output) data in this ETL testing check helps the user to ensure the completeness and accuracy of the data. For example,

  • Make sure the row count between the file and the table is matching.
  • Compare all the customer data in the source file and the table to ensure that ETL loaded the data in the destination table as per the mapping rules.
Automated ETL Testing Techniques Scenario 1 - iCEDQ

Scenario #2

An ETL process is reading data from a source customer table that has individual and organizational customers and loading only the individual customer and doing a lookup against a gender reference table.

Data Transformation ETL Testing

Testing Data transformations invoveinvolve reconciling the data between source and destination to verify that the ETL is transforming the data as expected. For example,

  • Make sure the ETL is filtering the data correctly by comparing filtered rows from the source with all the data in the target.
  • Test that ETL is transforming source gender values M and F into Male and Female.
Automated ETL Testing Techniques Scenario 2 - iCEDQ

Scenario #3

ORDER_ETL has successfully loaded the data into the Orders table and

SHIPMENT_ETL has loaded the data into the Shipments table. All the technical and functional tests created for the process were a success.

Business Reconciliation ETL Testing

A business reconciliation test helps classify all the data issues that occurred because a business rule was invalidated. This type of check is unrelated to functional testing of an ETL process but is more business-focused. For example,

  • Compare the shipment data with orders data to identify any shipments for the status of orders that are invalid or unsuccessful.
Automated ETL Testing Techniques Scenario 3 - iCEDQ

Top features of an ETL Testing tool

As organizations learn to adapt to an ever increasing amount of data volume and complexity of ETL processes, they are finding it tough to automate ETL testing, increase test coverage and reduce data issues at the same speed. To solve the above challenges, you need to select the right ETL Testing tool and enable your users to perform ETL testing efficiently.

Data-Comparison-Engine - iCEDQ

Data Comparison Engine

The most crucial piece of any ETL Testing tool is the ability to compare and validate high volume data across databases and files with different formats. Therefore, having a robust data comparison and validation engine which can analyze millions of rows, is a must have in your automation testing toolkit.

Graphical-User-Interface-iCEDQ

Graphical User Interface

It is necessary to provide users with a testing tool that does not require significant training. An ETL Testing tool should enable users to create sophisticated and automated tests as well as to reduce the time it takes to implement those tests. The ability to develop ETL testing checks with drag/ drop functionality with a few clicks results in significant productivity gains.

Data-Connectivity-iCEDQ

Data Connectors

Today, every organization has various data sources like Oracle, Teradata, Flat Files, Redshift, Snowflake, etc. Moreover, an ETL process might connect to any or all these data sources. Therefore, it is necessary to choose an ETL Testing tool that supports native integration with databases, files, and APIs.

CI CD pipeline integration with Jira and Jenkins-iCEDQ

CI/CD Integration

Most organizations have implemented DevOps and are looking to implement DataOps. Hence, any ETL Testing tool must be integration-ready with CICD tools so that organizations can quickly embed their testing as a part of their data pipeline.

Workflow-Integration-iCEDQ

Workflow Integration

One of the primary goals of any ETL Testing tool is to make it easier for users to automate the testing of their ETL processes. Test execution automation can be done using a scheduler, via ETL tools, or continuous tools. Therefore, having an ETL Testing tool that allows users to integrate the execution of these tests using external enterprise tools is an added benefit with increased efficiency gains across the organization.

Use iCEDQ for ETL Testing

  • Compare 100% of your data using our in-memory ETL Testing engine and get the best performance. Read More
  • Connect to any heterogeneous data source, be it a database, file, or an API to perform ETL Testing effectively. Find the complete list of connectors here. Read More
  • Collaborate with your team using our web-based app with an easy to use GUI to generate ETL tests, execute tests, and share the test results across the organization.
  • Integrate with test case management tools like HP ALM, defect logging tools like Jira, and CICD tools like Jenkins using our Rest API and CLI to automate your complete data pipeline.
  • Schedule ETL Testing rules using our inbuilt scheduler. If required, you can trigger the tests using an external scheduler too. You can read all about the iCEDQ features here. Read More
iCEDQ Engine

Use Cases