How to enforce rules for your data architecture?

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In our data-driven age, keeping data accurate and following rules are key tasks for any data solution architect.

CAT is revolutionizing data architecture by simplifying rule enforcement and testing,ensuring ongoing compliance, and preserving the integrity of diverse data systems.

Understanding Data Architecture and Its Challenges

Data architecture encompasses the models, policies, rules, and standards that governwhich data is collected, how it is stored, arranged, integrated, and used within anorganization. It serves as the blueprint for managing data flow and ensuring data is usedeffectively to support business operations and decision-making.

However, maintaining data integrity and compliance within this architecture can bechallenging. Common issues include:

  • Manual Efforts: Manually checking compliance can be time-consuming andprone to errors.
  • Rules Are NOT Followed: Rules such as naming conventions and mandatory columns are often documented but not enforced by tests.
  • Wasting your time and resources to fix related problems: Rules can be written as tests di

How CAT Simplifies Rule Definition and Enforcement

CAT addresses these challenges by providing automated solutions that ensure rule enforcement is precise, consistent, and requires minimal manual intervention. This makes it possible to define rules easily and ensure they are always followed.

Defining Rules with Simple SQL Statements

Using CAT, data architects can define rules using simple SQL statements. This reduces the burden of documenting rules in traditional formats like MS Office documents. For instance, mandatory columns can be specified directly within CAT using SQL, ensuring every table adheres to these rules.

Example rules might include:

  • Every staging table must have an IngestedTimestamp column with datetime2 datatype.
  • Table names must start with an uppercase letter and must not contain underscores.
  • Every fact table must be a clustered columnstore index.
  • All measures must exist in a Metrics table, with no measures defined outside of it.

Generating Tests from Metadata

CAT can automatically generate tests from metadata, drastically reducing manual effort. Metadata-driven tests ensure that the rules are not only defined but enforced consistently across all data objects. If the metadata of real objects diverge from predefined rules, CAT quickly identifies discrepancies.

Checking Any Data System

One of CAT’s standout features is its ability to check various data systems. Whether dealing with relational databases like SQL Server, Oracle, Power Bi, SSAS, Snowlake, and files such as MS Excel, YAML or CSV, or semantic models and datasets like Power BI and Analysis Services, CAT ensures compliance across the board. This flexibility is crucial for organizations with diverse data ecosystems.

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Benefits of CAT for Data Architects

CAT isn’t just a tool; it’s a comprehensive solution that brings multiple advantages to data architects.

Enhanced Rule Compliance

By automating the enforcement of rules, CAT ensures ongoing compliance with minimal intervention. Unlike manual documentation that often gets overlooked, CAT’s rules are enforced forever, even for objects created in the future. This continuous enforcement helps maintain data integrity over time.

Reduced Manual Effort

CAT’s ability to automatically generate tests and enforce rules significantly reduces the manual workload for data architects. Tasks that once required thorough peer reviews and checks are now handled seamlessly by CAT, saving valuable time and resources.

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Benefits for Developers Using CAT

Developers also benefit immensely from CAT’s capabilities.

Peer Reviews

Peer reviews are crucial but often take up a lot of time. CAT helps developers by automating compliance checks, making the process faster and easier.This ensures that no manual reviews are needed for rule enforcement, allowing developers to focus more on building and less on checking.

Early Notification of Rule Violations

Notification is key to preventing issues before they reach production. CAT alerts developers early in the development process if any rule violations occur. This proactive approach helps ensure that fixes are implemented sooner, preventing potential roadblocks and maintaining system integrity.

Conclusion

CAT is transforming the landscape of data architecture by automating rule definition and enforcement, enhancing compliance, and improving both architect and developer life by integrating CAT into their processes, data architects can ensure that rules are not just written but strictly followed, reducing manual effort and enabling a more streamlined, efficient development process. This makes CAT an invaluable tool in maintaining robust and compliant data architectures across diverse systems.

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