Integrity checks via Databricks

Given the context of your pipeline, where the source data from DB2 production systems is considered highly reliable and column-level checks (such as presence of mandatory fields, null validation, and type-safe transformations) are already in place, it is still prudent to consider additional data type-level validation and constraint-level checks within Databricks during transformation.

While the DB2 source may enforce certain constraints, there are several reasons to implement these checks within Databricks:

Data Integrity Assurance: Implementing data type-level validation and constraint-level checks within Databricks ensures that any anomalies or discrepancies introduced during the transformation process are caught early. This adds an extra layer of data integrity assurance, which is crucial for maintaining high-quality data throughout the pipeline.

Consistency Across Systems: By enforcing constraints within Databricks, you ensure that the data remains consistent across different systems and stages of the pipeline. This is particularly important when integrating with downstream systems like Azure SQL Hyperscale, where data consistency is critical for accurate analysis and reporting.

Error Handling and Debugging: Having validation checks within Databricks can help in identifying and isolating issues more effectively. If an error occurs, it is easier to pinpoint whether it originated from the source data or during the transformation process, facilitating quicker debugging and resolution.

Compliance and Governance: In certain industries, regulatory compliance and data governance policies may require multiple layers of validation to ensure data accuracy and reliability. Implementing these checks within Databricks can help meet such compliance requirements.

However, it is also important to weigh these benefits against the potential risks and costs associated with duplicating business logic and performing validations multiple times:

Risk of Drift: Duplicating business logic for validation can lead to a risk of drift, where the validation logic in Databricks may become out of sync with the source system's logic over time. This can result in inconsistencies and potential data quality issues. To mitigate this risk, it is essential to establish robust processes for maintaining and synchronizing validation logic across systems.

Cost and Latency: Performing validations multiple times can introduce additional computational costs and latency to the data pipeline. This can impact the overall performance and efficiency of the pipeline, especially for large datasets. It is important to assess the impact of these additional validations on the pipeline's performance and determine whether the benefits of enhanced data integrity outweigh the costs.

Architecture is about weighing risks and benefits and finding an appropriate compromise. In this case, you may consider implementing a selective approach to validation, where only critical checks that are most likely to catch significant issues are duplicated within Databricks. This can help balance the need for data integrity with the desire to minimize cost and latency.

To implement these checks, you can use Databricks' built-in capabilities for schema enforcement and data validation. Here are some steps you can take:

• Schema Enforcement: Define and enforce schemas for your data using Databricks' schema enforcement features. This ensures that the data adheres to the expected structure and data types.
• Constraint Checks: Implement primary and foreign key constraints, uniqueness constraints, and other business rules using Databricks' constraint enforcement capabilities.
• Data Validation: Use Databricks' data validation functions to perform checks on data values, such as range checks, pattern matching, and custom validation logic.

By incorporating these additional validation and constraint checks within Databricks, you can enhance the overall reliability and integrity of your data pipeline, ensuring smooth and accurate downstream ingestion into Azure SQL Hyperscale.

Skipping such row-level integrity checks could pose risks, such as data inconsistencies, undetected errors, and potential issues with downstream systems that rely on accurate and validated data. Therefore, it is advisable to implement these checks to mitigate any potential risks and maintain high data quality standards.