ETL Testing is essential in the modern business world to ensure accurate data for informed decisions. It safeguards data integrity throughout its journey, addressing complexities like diverse data formats and increasing volumes. ETL testing involves some important validations that are essential processes that ensure the accuracy, completeness, and reliability of data as it moves through various stages of the ETL pipeline.

So, what are the important types of Validations in Comprehensive ETL testing?

Important types of Validations in ETL Testing are,
• Mapping Doc Validation
• Metadata/Schema Validation
• Data Mismatch Validation Check

Mapping Doc Validation

It involves the verification and validation of ETL mapping documents to ensure they accurately represent data transformations and business rules. This process helps maintain data integrity and consistency throughout the ETL workflow.
a. Mapping Document Accuracy and Completeness: Mapping documents serve as blueprints for ETL processes, outlining how data is extracted, transformed, and loaded. Validating their accuracy and completeness is very important to ensure that data flows as intended. Any discrepancies in these documents can lead to data inaccuracies and misinterpretations.
b. Business Rule Validation: Business rules define the logic behind data transformations. This validation ensures that Business rules are correctly implemented and that the transformed data aligns with the expected business outcomes. It safeguards against errors that could impact decision-making and data-driven operations.

Metadata/Schema Validation:

This process involves verifying and ensuring the accuracy, consistency, and adherence to defined standards of attributes, data types, data lengths, and constraints within the data schema, which includes checks for attributes, data types, data lengths, and constraints.
a. Attribute Check:Attribute validation confirms that each data attribute or column adheres to the defined schema. It verifies that data is correctly structured and any deviations can be promptly identified and resolved.
b. Data Type Check:Data type validation ensures that data values match the specified data types. Mismatches can lead to unexpected behaviors and errors downstream in the ETL process.
c. Data Length Check:This validation process assesses the length of data values to avoid potential problems like data truncation or overflow, which could compromise data precision.
d. Constraints Check:Validating constraints includes verifying their proper application to designated tables and checking the integrity of different constraint types like UNIQUE, NOT NULL, Primary Key, Foreign Key, Check, and Default. This validation process ensures data consistency and integrity in the database, preserving its accuracy and reliability.

Data Mismatch Validation Check:

Data Quality Validation:
a. Data Completeness Check: Verifies that the expected number of records is present after each ETL step.
b. Column Data Profiling: Examines data distribution to detect missing or unexpected values.
c. Data Duplicate Check: Identifies and eliminates duplicate data entries that could skew analytical results.
Data Accuracy Check:
i. Non-numeric Data Types: In this category, we ensure the precision of non-numeric information such as emails, postal codes, and phone numbers, validating their adherence to a valid format.
ii. Domain Analysis: We select data domains and validate them for errors using the following criteria:
a. Value-Based: We compile a list of permissible values for a specific column in a table and then confirm if the column values are a subset of this list. For instance, we verify if the Gender column contains either ‘Male (M)’ or ‘Female (F).’
b. Range-Based: We establish minimum and maximum value ranges for valid data entries in a column, guided by logical or business considerations. Subsequently, we validate if the column values fall within the specified range. For example, we might set the acceptable Age range as 0 to 150.
c. Reference File: The system employs an external validation file, such as one containing country codes.
Here, we verify if:
The system selects the correct value from the reference file.
The country codes match between the QA and Production environments.
If there have been updates to country codes in the reference file, we confirm that these updates are accurately reflected in the database.
Data Consistency check :
Data consistency checks, whether within the same schema or across different schemas, validate the integrity of data, ensuring that data relationships are preserved during transformations. Additionally, data uniformity tests are conducted to verify that the entity’s actual value consistently matches across various locations

Data Transformation Validation:

This validation confirms that data transformations are applied accurately, adhering to business logic and rules. It safeguards against the introduction of errors during data processing. The steps involve reviewing the source-to-target mapping document for transformation understanding, applying SQL queries to implement ETL logic on source data, and finally, comparing the transformed test data results with the data in the target table.

Data One to One Validation (As-Is Mapping):

Do you believe that mapping document validation, metadata validation, data quality validation and data transformation validation are sufficient, or should we also validate the mapping data?
Yes, certainly, it’s crucial, considering that approximately 80% of the data relies on ‘as-is’ mapping
One-to-one validation, commonly known as ‘as-is’ mappings, is a vital process that guarantees data remains unaltered throughout the ETL process to maintain its integrity. This validation involves the use of minus queries, which entail performing both ‘source minus target’ and ‘target minus source’ comparisons. Any values returned by the minus queries are considered as mismatching rows.

Conclusion

ETL Testing plays a crucial role in ensuring accurate and reliable data for informed decision-making. It involves several important validations:
Mapping Doc Validation: Verifying ETL mapping documents for accuracy and completeness to maintain data integrity and business rule adherence.
Metadata/Schema Validation: Checking attributes, data types, data length, and constraints to ensure data structure consistency and reliability.
Data Mismatch Validation Check: Focusing on data quality, including completeness, accuracy, and consistency, along with one-to-one validation to prevent data alteration.
These validations collectively safeguard data quality, making it suitable for efficient data-driven operations and informed decision-making in the modern business landscape.
At Blismos Solutions, we excel in executing these vital validations. They are integral to our core capabilities, strengthening data quality as our top priority. This dedication ensures that the data we provide is optimally tailored for effective data-driven operations and informed decision-making in today’s dynamic business environment. Trust Blismos Solutions to safeguard your data’s integrity.