Bricks ETL Testing OnePage Document

 

Context:

Current Process:

In the current environment of our client, ETL / Data testing are conducted exclusively through manual processes. This approach is not only time-consuming but also prone to human error. Furthermore, manual testing does not allow for comprehensive field-level validation, which is crucial for ensuring data integrity and accuracy. As a result, the overall efficiency and effectiveness of the testing process are significantly hampered.

 

Proposed Solution

To address these issues, we have proposed automating the ETL testing process using PySpark. PySpark offers robust data processing capabilities that can handle extensive data validations efficiently. Our solution includes the following key features:

1. Record Count Validation: Ensures the number of records in the source matches the target.

2. Field-Level Validation: Verifies that individual fields/columns are correctly transferred and transformed.

3. Null Validation: Checks for the presence of null values where they shouldn't exist.

4. Duplicate Value Check: Identifies duplicate records that may compromise data quality.

5. Schema Validation: Confirms that the schema of the data in the source and target are consistent.

 

The PySpark scripts performing these validations are integrated into AWS Glue, an ETL service that prepares and transforms data for analytics. The integration process involves the following steps:

 

1. Data Extraction: Data is extracted from the source, an S3 bucket.

2. Transformation and Validation: PySpark scripts execute various validation checks during the transformation process.

3. Loading: The transformed and validated data is loaded into the target, which is a Snowflake database.

4. Result Storage: After the job is completed, validation results are generated in CSV format and stored in an S3 bucket for further analysis.

 

Benefits

Implementing this automated solution offers several benefits:

1. Time Efficiency: Automation significantly reduces the time required for testing, allowing for quicker turnaround and more frequent testing cycles.

2. Accuracy and Consistency: Automated tests minimize human error, ensuring more reliable and consistent validation results.

3. Comprehensive Validation: Field-level validation and other checks ensure a thorough examination of data integrity, which is challenging to achieve manually.

4. Scalability: The solution can easily be scaled to accommodate larger datasets and more complex validation requirements without a proportional increase in testing effort.

5. Cost Savings: Reduced manual effort translates to lower labor costs and resource allocation for testing activities.

 

Conclusion

The proposed solution leverages PySpark and AWS Glue to automate the ETL testing process, addressing the limitations of manual testing. By implementing this solution, the client can achieve more efficient, accurate, and comprehensive data validation, ultimately leading to better data quality and operational efficiency. The integration of automated testing within the ETL pipeline ensures that the data integrity is maintained consistently across the ETL process, providing reliable outcomes for the project. With data sources in S3 and the target in Snowflake, this solution enhances the overall data management strategy for Bricks.