In recent years, the competition for data solution tools has heated up. While AWS, Azure, GCP and many more companies investing heavily into Data Engineering such as AWS Glue Studio, Azure DataFlow, GCP cloud data fusion. While most companies focusing on low-code and drang-n-drop path. However, DBT (Data Build Tool) takes a different approach by embracing software engineering principles.
Instead of opting for the easy path, DBT proposed the right way of doing things, grounded in sound engineering practices. we will explore why DBT is different from big giants but today let's dive into part of it.
Table of Contents
- Introduction
- Audience
- Software Engineering
- Limitations of Today's Data Pipelines
- DBT's Adherence to Software Engineering Practices
- Conclusion
Introduction
In this Post, we'll explore Software Engineering methods used in DBT (Data Build Tool). While a basic understanding of DBT's features from its documentation might suffice for contributing to a project.
So, why read this? Well, we'll explain the Software Engineering methods used by DBT and why they matter, In short, we'll uncover the reasons behind DBT's features.
That's make difference cause knowing reason and potential of feature is much more important than just mastering any feature, if you violate the reason or core of the feature, than that's feature is killed and it's just another workaround or patch.
Audience
Software Engineering
The realm of software engineering holds a vast history, having witnessed the contributions of numerous scientists and professionals.
Their collective efforts have propelled software methodologies to new heights, constantly striving to surpass previous achievements while upholding an audacious spirit.
Software engineering stands as the bedrock of modern technological advancements, weaving a rich tapestry of methodologies and
practices that shape the way we design, develop, and maintain software systems.
Its roots stretch back to the mid-20th century, evolving from simple programming to a comprehensive discipline encompassing various principles, tools, and frameworks.
Over the decades, software engineering has propelled innovations, enhancing reliability, scalability, and maintainability of systems across diverse industries.
Limitations of Today's Data Pipelines
In the realm of big data, the sophistication of data pipelines has surged, enabling the handling of massive datasets.
However, conventional data pipelines often exhibit limitations. They are prone to complexities, becoming intricate webs of disparate scripts, SQL queries, and manual interventions.
These pipelines lack standardization, making them difficult to maintain and comprehend. As the data grows, managing these pipelines becomes a daunting challenge, hindering scalability and agility.
DBT: A Solution Rooted in Software Engineering
Enter DBT (Data Build Tool), a paradigm shift in the world of data engineering that embodies the core principles of software engineering.
DBT redefines the way data pipelines are built and managed, aligning itself with established software engineering practices to tackle the challenges prevalent in traditional data pipelines.
DBT, stands as a revolutionary force in the domain of data transformation.
It reimagines the handling of data by infusing principles of agility and discipline akin to those found in the software engineering realm.
By treating data transformation as a form of software development,
DBT enables the scalability and seamless management of significant data components, facilitating collaboration among large teams with
unparalleled ease.
DBT's Adherence to Software Engineering Practices
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- DBT distinguishes between data transformation logic and data modeling, allowing for modularization and easier management. For instance, SQL queries in DBT focus on transforming raw data, while models define the final structured datasets.
- DBT has divided the usual data transformation into four parts: 1. Business logic (DQL), 2. Materialization (DDL & DML), 3. Testing, and 4. Documentation. These four areas now scale and maintain independently. Also, they are easier to read. Analytics engineers can focus on one thing separately. For example, they can solely concentrate on business logic (just select statements) while writing models. How to store or test or document have different section.
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Benefits
- Enhanced Maintainability
- Improved Reusability
- Better Collaboration
- Scalability and Flexibility
- Security and Risk Mitigation (Individual can models can have access control and owner)
- Future-proofing
- Reduction of Complexity
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- Just as software modules can be reused, DBT promotes reusable code blocks (macros) and models. This allows data engineers to build upon existing components, fostering efficiency and consistency. Also DBT has good amount of packages that can import and use directly in projects, allowing share standard and tested expression at glob.
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Benefits
- Efficiency
- Consistency and Standardization
- Ease of Maintenance
- Cost-Effectiveness
- Facilitates Collaboration
- Future-Proofing
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- Similar to software unit tests, DBT enables data engineers to create tests to validate the accuracy of transformations, ensuring data quality throughout the pipeline. You can test each of your single transformation (a model) before subsequent step run.
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Benefits
- Error Identification in Isolation: It allows the testing of individual components (units) of code in isolation, pinpointing errors or bugs specific to that unit. This facilitates easier debugging and troubleshooting.
- Enhanced Code Quality: Unit tests enforce better coding practices by promoting modular and understandable code. Writing tests inherently requires breaking down functionalities into smaller, manageable units, leading to more maintainable and robust code.
- Regression Prevention: Unit tests serve as a safety net. When modifications or updates are made, running unit tests ensures that existing functionalities are not negatively impacted, preventing unintended consequences through regression testing.
- Facilitates Refactoring: Developers can confidently refactor or restructure code knowing that unit tests will quickly identify any potential issues. This flexibility encourages code improvements without the fear of breaking existing functionalities.
- Improved Design and Documentation: Writing unit tests often necessitates clearer interfaces and more detailed documentation. This leads to better-designed APIs and clearer understanding of how code should be used.
- Accelerates Development: Despite the initial time investment in writing tests, unit testing can speed up development by reducing time spent on debugging and rework. It aids in catching bugs early in the development cycle, saving time in the long run.
- Supports Agile Development: Unit tests align well with agile methodologies by promoting frequent iterations and continuous integration. They facilitate a faster feedback loop, allowing developers to quickly verify changes.
- Encourages Modular Development: Unit tests require breaking down functionalities into smaller units, promoting a modular approach to development. This modularity fosters reusability and simplifies integration.
- Boosts Confidence in Code Changes: Unit tests provide confidence when making changes or additions to the codebase. Passing tests indicate that the modified code behaves as expected, reducing the risk of introducing new bugs.
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- The abstraction principle involves concealing intricate underlying details while presenting a simplified and accessible interface or representation. In DBT, for instance, model files encapsulate solely business logic, abstracting materialization and test cases. This seemingly simple feature proves immensely helpful. It's akin to skimming a newspaper headline—if more details are needed, delve deeper; if not, move swiftly to the next topic.
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Benefits
- Simplification of Complexity
- Enhanced Readability and Understandability
- Focus on Higher-Level Concepts
- Reduced Cognitive Load
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- The coupling principle refers to the degree of interconnectedness or dependency between different components or modules within a system. Lower coupling indicates a lesser degree of dependency, while higher coupling suggests a stronger interconnection between components.
- In DBT, managing coupling involves reducing dependencies between different parts of the data transformation process. Lower coupling is desirable for several reasons.
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- DBT facilitates comprehensive documentation for data models and transformations, akin to software documentation. This documentation aids in understanding the data flow, enhancing collaboration and knowledge sharing.
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- In the software world, it's common to use different environments like Development (Dev), User Acceptance Testing (UAT), and Production (Prod) to manage changes effectively and ensure stability. This practice, known as Environment Separation, helps isolate changes, allowing teams to test and validate new features or fixes in a controlled setting before exposing them to real users.
- It mitigates risks, ensures consistency, and facilitates compliance and security. Similarly, dbt (data build tool) seamlessly supports environment separation, allowing teams to define and manage different environments such as Dev, UAT, and Prod. This practice promotes better DataOps by ensuring that data transformations are thoroughly tested and validated before they impact production, improving reliability and reducing the risk of errors.
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- Clients often provide new requirements, or we may discover more optimal ways to perform tasks. When this happens, we tend to modify our existing models or queries. However, in a large project, a single query might be relied upon by many clients, making it challenging to notify all teams of changes.
Additionally, new changes can sometimes introduce faults, which can disrupt data pipelines and violate one of the core principles of big data: availability.
To address this, the software industry already employs strategies to manage such issues effectively. dbt (data build tool) supports different model versions, allowing teams to maintain multiple versions, such as a pre-release version for testing and a stable version for production use.
This versioning approach makes dbt highly adaptive, enabling teams to migrate to new versions at their own pace. Furthermore, dbt allows setting a deprecation period, specifying how long an old API version will be supported before it is phased out, aligning with the concept of a Deprecation Policy.
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Benefits
- User Experience Stability
- Reduced Migration Costs
- Minimized Downtime
- Flexibility in Adopting Updates
- Flexibility in Adopting Updates
- Encourages Innovation
- Risk Mitigation
Conclusion
DBT's fusion of software engineering principles with the domain of big data revolutionizes how data pipelines are conceived, constructed, and maintained. By embracing the tenets of software engineering, DBT addresses the shortcomings of traditional data pipelines, ushering in a new era of efficiency, reliability, and agility in data engineering. As software engineering continues to evolve, its synergy with big data technologies like DBT paves the way for more robust, scalable, and manageable data ecosystems.
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