Data engineering has evolved dramatically over the years, crystallizing into an intricate web of responsibilities. Today’s professionals often find themselves juggling multiple aspects of data management, which can lead to inefficiencies and confusion. Bernd Wessely’s seminal article in “Towards Data Science” calls for a redefinition of data engineering to better serve modern enterprise IT environments. As the demands of technological landscapes shift and complexities grow, it has become imperative to reconsider and reallocate responsibilities within data engineering to enhance efficiency and maintain high software quality.
The Current State of Data Engineering: Blurred Boundaries
Data engineering, as traditionally defined, encompasses developing, implementing, and maintaining the systems and processes that convert raw data into useful information for analysis and machine learning. This broad description has created a catch-all role that often blends technical tasks with the implementation of business logic. Consequently, data engineers shoulder a wide range of duties, including security, data management, DataOps, data architecture, orchestration, and even aspects of software engineering.
This hybridization of roles leads to significant complications. Data engineers find themselves entangled in tasks that are better suited for application developers, like applying business logic. This not only stretches their expertise thin but also results in brittle pipelines laden with uncoordinated and sometimes incorrect business logic. The current state of data engineering thus requires a re-evaluation to ensure efficiency and clarity. By overloading data engineers with an array of responsibilities that extend beyond technical data manipulations, organizations risk undermining the quality and maintainability of crucial data systems. It is important to recognize that while data engineers play an indispensable role in the technological ecosystem, the demand to wear multiple hats within the same job function can lead to systemic inefficiencies and potential data mismanagement.
The Core Problem: Business Logic in Data Engineering
One of the most contentious issues in the data engineering landscape is the implementation of business logic. Business logic governs the operations that define how data is processed and transformed to meet specific business needs. Ideally, these tasks should be handled by application developers who are intimately familiar with the business requirements and goals.
However, the reality is different. Data engineers often become the de facto executors of business logic, embedding these rules within data pipelines. This misallocation of responsibilities leads to pipelines that are not only complex but also prone to errors and difficult to maintain. These pipelines also lack the software quality and rigor expected from full-fledged applications. The core issue, therefore, is the need to segregate business logic from data handling tasks, ensuring each role can focus on its core competencies. By placing the burden of business logic on data engineers, not only does the potential for error increase, but it also dilutes their focus from technical data manipulations, which should be their primary concern. Application developers, being more closely aligned with business requirements, are better positioned to embed business logic within applications, thereby ensuring consistency and reducing the risk of misaligned data transformations.
The Proposed Redefinition: Separation of Concerns
To counter this muddled state of affairs, Wessely proposes a clear separation of concerns within data engineering. He suggests that data engineers should concentrate exclusively on the movement, manipulation, and management of data, eschewing any involvement in business logic. This redefinition focuses on purely technical manipulations like partitioning, bucketing, reformatting, normalizing, and indexing.
By limiting data engineering to these technical tasks, organizations can ensure that data pipelines are robust, efficient, and easier to maintain. Application developers, on the other hand, should take on the responsibility of implementing business logic, drawing on their deeper understanding of business needs. This segregation will lead to more coherent and high-quality systems, wherein each team can excel in its designated role. This new delineation of responsibilities promises to bring about a more streamlined approach to data handling, where the technical excellence of data engineers will shine without the complexities of uncoordinated business logic. Furthermore, by having application developers focus on business logic, the resulting systems will more accurately reflect organizational goals and be easier to troubleshoot and evolve over time.
Historical Context: The Evolution of Data Engineering
Understanding the rationale behind this proposed redefinition requires a look back at the historical development of databases and data engineering roles. Initially, databases were simple storage solutions, but over time, they evolved into multifunctional systems capable of handling not just data storage but also complex business logic and processes. This expansion turned databases into comprehensive platforms for software development, effectively merging the roles of data engineers and application developers.
The advent of big data tools like Hadoop presented an opportunity to revert to a clearer division of roles. Despite this, the complex nature of modern data stacks contributed to the persistence of the expanded scope of data engineering. As databases continued to evolve, so did the expectations from data engineers, who found themselves handling an ever-growing array of tasks. The historical blending of business logic with data handling arose from the multifunctional evolution of databases, making it apparent that a clear separation of responsibilities is crucial to maintain quality and efficiency in modern data engineering practices. As technological advancements propel the need for specialized roles, it becomes clear that returning to a divided structure will foster better-maintained and more precise data systems.
The Modern Data Stack: Complex but Manageable
The modern data stack, although providing numerous tools and technologies for data transformation, analytics, and business intelligence, still reflects the complex state of data engineering. While these tools offer powerful capabilities, they also demand data engineers to possess a wide range of skills, blending technical data manipulation with business logic implementation.
The idea of self-serving data platforms, as proposed in the data mesh framework, offers a promising solution. In this framework, data engineering focuses on providing data infrastructure that supports seamless data exchange and sharing, while business logic remains strictly within application domains. This approach aims to streamline the roles, allowing data engineers to build robust systems without the added complexity of business logic. The data mesh framework’s promise of treating data as a product signifies a potential turning point where application developers assume the mantle of business logic execution. Consequently, data engineers can invest their expertise exclusively in crafting strong, scalable, and efficient data infrastructures, ultimately benefiting the entire enterprise IT environment. Embracing this separation is not just an operational improvement but a strategic shift that aligns with the increasing need for precise and adaptable data systems.
The Path Forward: Building Robust Data Infrastructures
Data engineering has seen significant evolution over the years, transforming into a complex web of responsibilities. Modern professionals in this field frequently find themselves managing multiple facets of data management, a situation that can lead to inefficiencies and confusion. In his influential article for “Towards Data Science,” Bernd Wessely argues for a redefinition of data engineering to better fit the needs of contemporary enterprise IT environments. As the demands on technology grow and become more complicated, it is crucial to reconsider and reassign the roles and responsibilities within data engineering. This reevaluation aims to enhance both efficiency and the quality of software produced.
Data engineers today must not only understand data architecture and storage but also handle data integration, transformation, and governance. These expanded roles can dilute focus and lead to decreased productivity. By redefining the scope of data engineering, organizations can ensure that professionals specialize in specific areas, fostering deeper expertise and more robust solutions. This approach would streamline processes and minimize the chances of overlap and redundancy.
Moreover, as businesses increasingly rely on big data for decision-making, the importance of a well-defined data engineering strategy cannot be overstated. Proper role allocation will enable teams to better manage the enormous volumes of data they handle daily, ensuring that data is both high-quality and quickly accessible. By adopting Wessely’s recommendations, companies can navigate the intricate demands of modern technology landscapes more effectively, driving innovation and maintaining high standards of software quality.
