How can you ensure your data validation framework works with different platforms?

Open-source validation tools often have a numerous users contributing to their development. It will be good; they can provide ongoing support and updates timely. The tool should provide cross-platform compatibility, flexibility, and adaptability along with the support of handful databases and data formats like CSV, JSON, XML etc. Additionally, we can containerize the tool so no need to worry about dependencies. Select a validation tool that offers customizable configurations as per our requirements and project needs, which promotes flexibility.

Important is the tool to be compatible with your existing data infrastructure. It should work seamlessly with your databases, data warehouses, data lakes, etc. I suggest you to consider whether the tool can scale as your data grows. It should be able to handle increasing volumes of data without a significant impact on performance.

When selecting a data validation tool, several factors must be carefully considered. Cross-platform compatibility is key, but it is equally important to meet the requirements of data processing workflows. Open source tools like Deequ offer flexibility, while data platform tools like Databricks offer scalability. It is best to integrate open source tools with data platform tools - as I have done with Deequ and Databricks., allowing seamless integration of data validation into all data processing capabilities of Databricks.

Deequ shines in data validation for these reasons: 1. Open Source Customization: An open-source library, empowering teams to tailor it. 2. Seamless Scalability: Built for large datasets, seamlessly integrates with Apache Spark for efficient and scalable validation. 3. Tailored Rules and Metrics: Users define rules and metrics, ensuring adaptability to specific validation needs. 4. Automated Monitoring: Supports continuous data quality monitoring with automated checks for proactive issue resolution, enhancing overall reliability.

Ensuring that your data validation framework works across different platforms involves designing a system that is adaptable, scalable, and compatible with various technologies. Here are some general strategies and best practices to achieve platform independence for your data validation framework: Abstraction and Standardization: Use abstraction layers to separate platform-specific details from the core logic of your data validation framework. This allows you to create a standardized interface that can be implemented differently for each platform. Leverage widely accepted standards and formats for data representation and exchange (e.g., JSON, XML, CSV) to enhance interoperability. Cross-Platform Libraries:

One of our work-focus was on Business Logic-Level Rules. These rules validate your data based on specific business requirements. 1.Uniqueness Check: Checks if a value (or a combination of values) is unique across the dataset. 2.Referential Integrity Check: Checks if a foreign key value exists in another table. 3.Custom Business Rules: Any rule specific to your business needs. For example, if you’re validating sales data, you might have a rule that the ‘Sales Amount’ field must be greater than zero

As you have already nicely pointed out, there are different types of validation rules, and not all of them are static, but can move in a range of, e.g. x% of the 30-day average. More complex rules, especially when other data objects are used, e.g. Referential Integrity Checks come with a high price tag.

Defining robust validation rules is an important step in ensuring the integrity of your data validation framework across different platforms. Robust validation rules that span across schema, content and business logic levels serve as benchmarks that your data must meet for successful validation. An often overlooked aspect is the dynamic nature of these rules. While static rules target fixed criteria, dynamic rules adapt to changing contexts and allow for more nuanced validation. This adaptability can be a decisive advantage, especially in dynamic data ecosystems. The data platforms should allow validation rules to seamlessly adapt to changing data structures and provide a mechanism to adapt to variations in data layout/schema.

I'd say that business rules are to be defined by business itself, not data engineers. Some enterprise level tools support business users - data stewards, domain owners, etc., to implement their rules directly. Outside of those, data engineers can implement the rules wherever, but definitions are to be given by business. Technical quality should be enabled by DE team directly: null values, data formats, data typing. Throw some anomaly detection based on standard deviation of row counts or proportions of values, and you will get an effective coverage.

I feel SQL validation framework is more scalable and platform independent. For example you can write a SQL query to pull the no of records, Null values and data type. Now your data pipeline can depend on above sql one only run if values from above SQL is within range.

For an automated perspective i use to define all the data validation rules in a metadata file that can be fetched by ADF. Validation rules are basic step to follow and to ensure data integrity, consistency, quality wise are necessary and not compromised while you ingest the data. Frequency can be specific to the business as well as data requirements.

In my opinion, exception handling is a very critical point, especially from a business perspective. What happens if: - one record is bad? - 10% of the records are bad? - 30% of the records are bad? Do you sort out the bad records? At what point does processing stop? These decisions have a massive impact on the e2e business flow.

The critical phase of implementation involves writing code that executes the defined rules and generates validation results. An alternative is to use a modern data platform that automatically executes validation rules, such as Databricks with DLT expectations. Databricks DLT (Data Live Tables) expectations define rules and constraints for data tables within the lakehouse architecture. These expectations ensure the structural and semantic integrity of the data and provide a powerful mechanism to validate and enforce data quality, consistency and compliance with predefined standards across different platforms and data sets.

In my experience using AWS data Brew is also a clasic example of automation of checking incoming data to your data lake Utilizing AWS DataBrew, I have been able to revolutionize the way we approach data quality within our data lake. By defining custom rules and conducting thorough data quality checks, AWS DataBrew enables us to ensure the consistency and accuracy of incoming data. This innovative approach allows for the automated identification and correction of flawed or inaccurate data, whether it involves removing, replacing, or rectifying specific datasets. The result is a significant enhancement in the reliability and integrity of data, directly contributing to more informed decision-making and streamlined operations.

In my current role as a principal data analyst, we worked on building a generic Data Quality framework using complex sql validation rules for metrics and KPIs in-terms of key pillars like completeness, correctness, accuracy, timeliness and validity. Rules under each pillar translates into a bunch of sql statements based on what we are trying to check. This framework has its own version of underlying data model to store the results and key metrics across slices and dices. Both the framework and the underlying model are going to be platform agnostic and scalable over time. It’s now as simple as moving this framework to any platform like AWS infra, azure infra, snowflake infra etc and its going to work seamlessly.

As part of testing, remember to include a robust mechanism for change management. How will your validation rules hold up to increasing data volumes? How will they need to be adapted for new fields and new rules? This can be informed through data governance, which should be part of your thought process when considering validation frameworks.

In addition to selecting tools that are compatible with different platforms, you should also consider cross-platform validation rule standards. Standardize validation rules to accommodate different data environments and data platforms. Look for adaptability and enable dynamic validation adjustments based on context changes. Integrate collaborative elements and encourage community-driven validation improvements. Investigate aspects such as incorporating machine learning models to predict potential validation issues.

Here are a few nuggets to consider: Design your validation framework with schema flexibility, allowing it to adapt to changes across platforms. Implement robust error handling for platform-specific issues, facilitating effective debugging. Prioritize version control to keep the framework aligned with evolving platform technologies. Provide comprehensive documentation for users to configure the framework seamlessly on different platforms.

The definition of an interface contract that describes the formats and valid values is level 101! My favorite example is a simple timestamp. Is it local (with the fun of summer/winter time changes) vs. UTC?

Creating effective validation rules is a balancing act. Focus on user experience by providing clear, actionable error messages. Prioritize security to guard against threats like SQL injection and XSS. Keep rules simple for easier maintenance and updates. Consider internationalization; accommodate different formats for dates, numbers, etc. Lastly, thoroughly document your validation logic for clarity and future reference. This approach ensures a secure, user-friendly, and maintainable system.