ETL is a process of Extracting (E) data from source systems, Transforming (T) the data based on business requirements and then Loading (L) the processed data into the destination database. Enterprises have thousands such ETL jobs running daily or under development. Most of these ETL processes are seen in Data Warehouse, MDM, Data Migration, and Big Data Projects.
These ETL jobs are processing critical business data such as financial data, marketing data, and customer data. But with time the number of new challenges in the development of ETL as well as testing of the ETL process has propped up.
- 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
Data is critical for the business. And bad data is a direct result of incorrect ETL processes. Therefore, it is essential to develop and test the ETL processes in a very systematic manner.
What is ETL Testing?
ETL Testing is a process of verifying an ETL process by validating and comparing the input data and output data along with the transformation.
ETL Testing Challenges
No graphical user interface
ETL process are background jobs that run inside a black box; at run time there are no graphical user interface; therefore, testing the ETL process is not straightforward. It also rules out any conventional testing products for testing ETL.
Manual data compare
Manual data compare by eyeballing it, which is highly error-prone. This manual process also forces the user to work with samples of data. Full volume testing (Big Data) is not possible with a manual approach.
Comparing the data across different database and/or files
Comparing the data across different database and/or files. Users must convert the data format and load it in excel for testing it, which causes its challenges.
Regression Testing becomes another challenge if there is no automation involved in ETL Testing. Ability to run old and new tests together gives much power to the user.
Test Data Management
Test Data Management is another concern. Creating sample test data can cover only some of the test cases. But in reality, only after processing millions and billions of records, you can be sure that all the test cases are covered.
Check the detailed article on “How ETL Testing is different from application testing?”
Type of ETL Testing Checks
Here are some of the challenges in testing ETL process:
Schema Validation Tests
It helps verify the metadata of the data source.
- Check if the column name is the same between two sources
- Check if the datatype is and length is as expected for a column
- Make sure that a table an index on the specified list of columns
Data Validation Tests
Data Validation Tests is used to validate a single data source, be it a database table, file extracts, dimension table, or a fact table.
- Check if there are any nulls in the name column.
- Format of the email should be valid.
- There should be only one active record in a dimension table.
- Date in birth date column should be a valid date.
Source vs. Target Data Comparison Tests
Source vs. Target Data Comparison Tests test ensures that data has been loaded to the target system as is without any issues.
- Verify the table count between source and target.
- Make sure all the data in the source file is loaded in the table, as is.
Data Transformation Tests
Data Transformation Tests ensures that every row has transformed successfully based on the mapping document.
- Test the transformation of first name and last name source column into full name target column.
- Check if M and F values in the source converted into Male and Female in the target.
Business Data Validation Tests
Business Data Validation Tests is used to check the validity of the data based on business requirements.
- Verify that the sales amount calculation does not break any business rule
Business Data Reconciliation Tests
An ETL process is programmed correctly and executed correctly. But the if the source system has not provided the data as expected. This will also cause incorrect data. For example, if the order and shipment data is being loaded by two ETL processes. It is possible to have shipments without the presence of order. Business data reconciliation tests will capture those situations.
ETL Process Orchestration Tests
In a batch, ETL processes are executed in a specific order or event. Even if the ETL processes are correct but executed in the wrong order you will get incorrect data. Hence some of the ETL tests must be designed to validate the order or sequence of execution.
To know more about ETL Process, read our blog post “How to test an ETL Process”.
What to Look for when Buying an ETL Testing Tool?
As organizations learn to deal with an increasing amount of data volume and complexity of ETL processes, they are finding it challenging to automate ETL testing, increase test coverage and reduce data issues at the same speed. To solve the above challenges, you must choose the right ETL Testing tool and enable your users to perform testing efficiently.
Data Comparison Engine
The most crucial piece of any ETL Testing tool is the ability to compare and validate data across databases and files with different formats. Also, allowing the user to compare a high volume of data with ease. Therefore, having a robust data comparison and validation engine is a must. Don’t confuse data comparison engine with some utility that loads data in a database and uses a database like MySQL as a comparison engine.
Big Data Processing
There is a vast difference between testing conventional data vs. big data. To test data in the standard environment you pull data to the processing engine. For big data testing, there is no way you can extract terabytes of data over the network apply your test. For big data, the testing process must be push test execution to the cluster.
Every organization today has various data sources like Oracle, Teradata, Files, Redshift, Snowflake, and many more. Moreover, an ETL process might connect to any or all these data sources. Therefore, choose an ETL Testing tool that supports native integration with databases, files, and APIs.
Graphical User Interface
It is necessary to provide users with a tool that does not require much training. The ETL Testing tool should enable them to create sophisticated tests and reduce the time it takes to implement them. Ability to create these tests with drag/ drop and few clicks is beneficial for all the users.
One of the intended goals of any ETL Testing tool is to make it easier for users to automate the testing of these ETL processes. Test execution automation can be done using a scheduler, 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 a plus.
DevOps principles require continues to build, integrate, test, deploy and monitoring requirements. The ETL Testing tool must support the integration with CICD tools and have APIs to create ETL Testing rules, execute testing rules and post the results back into tools like Jenkins.
Automate ETL Testing with iCEDQ
- Use our ETL testing engine and compare the full volume of your data in memory and get the best performance.
- Run on cloud, hybrid or on-premise.
- Use the standard edition, High throughput engine or the Big data engine powered by Apache Spark.
- Compare data across different platforms be it a traditional relational database, flat files, XML files, Rest API, cloud data warehouse, or any other data source.
- Collaborate using our web-based app to generate ETL tests, execute tests, and share the results across the organization.
- Integrate with test case management tools like HP ALM, TFS, defect logging tools like Jira, Service Now, and Continuous Integration tools like Jenkins and Bamboo and automate your complete data pipeline.
- Read the complete list of features iCEDQ offers.