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Discover the essential best practices for implementing DataOps architecture, boosting productivity, and achieving error-free deployments
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DataOps is revolutionizing the way businesses manage and deploy data workflows, ensuring error-free production and faster deployment cycles. BERGH et al. (2019) outlined seven key best practices to implement a robust DataOps architecture. These steps, when executed effectively, can boost team productivity, enhance automation, and mitigate risks in data projects. Let’s dive into these practices and uncover how they can help businesses thrive in the era of DataOps.
1. Add Data and Logic Tests
Preventing errors in production and ensuring high-quality data are critical in DataOps. Automated tests build confidence that changes won’t negatively impact the system. Tests should be added incrementally with each new code iteration.
Key types of code testing include:
Unit Tests: Targeting “individual methods and functions of classes, components, or modules” and are easy to automate and cheap (Pittet, 2022).
Integration Tests: Ensure proper integration of services or modules.
Functional Tests: Focusing on the fulfillment of user or business requirements by “verifying the output of an action and do not check the intermediate states of the system when performing that action” (Pittet, 2022)
2. Use Version Control Systems
Version control systems, such as Git, are central to any software project and especially crucial in DataOps. Key benefits include:
Saving code in a known repository for easy rollback during emergencies.
Enabling teams to work in parallel by committing and pushing changes independently.
Supporting branch and merge workflows, which allow developers to create separate branches for testing new features without affecting production code.
By isolating development efforts, version control simplifies collaboration and increases productivity.
3. Multiple Environments for Development and Production
Using separate environments for development and production is essential. These environments act as isolated spaces, ensuring changes in one do not affect the other.
The production environment can serve as a template for development, enabling seamless transfer of configurations.
This approach supports continuous integration and continuous deployment (CI/CD) by ensuring configurations match across environments without manual intervention.
4. Reusability and Containerization
Containers streamline microservices by isolating tasks and defining clear input/output relationships (e.g., REST APIs). Benefits include:
Increased maintainability: Changes in one container do not affect others.
Scalability: Containers can balance load by replicating when data volumes surge.
5. Parameterization
Parameterizing workflows improves efficiency by tailoring deployments based on specific requirements. For instance, parameterized configurations can adapt seamlessly between development and production environments.
6. Automation and Orchestration of Data Pipelines
Ensuring smooth, automated data flow from ingestion to delivery. Are you interested in more information about Data Pipelines. Take a look here: LINK
7. Avoid fear and heroism in DataOps projects
With an increase of automation (e.g., infrastructure provisioning and workflow orchestration) and automated testing of code as well as firefighting activities can be reduced significantly. Heroism - like working on weekends – as well as fearing or just hoping production model is not going to crash can be avoided. Results in a stable and reliable production and long term retention of technical talents.
Implementing these seven best practices is essential for building a successful DataOps architecture. From automated testing to containerization, these strategies empower teams to work more efficiently, reduce risks, and achieve scalable, error-free deployments. By adopting these steps, businesses can unlock the full potential of DataOps and stay competitive in a data-driven world.
Bergh, C., Benghiat, G., & Strod, E. (2019). The DataOps Cookbook Methodologies and Tools That Reduce Analytics Cycle Time While Improving Quality Second Edition. The DataOps Cookbook | DataKitchen
Pittel, S. (2024, Dec 20) different types of software testing. https://www.atlassian.com/continuous-delivery/software-testing/types-of-software-testing
Densmore, J. (2021). Data Pipelines Pocket Reference: Moving and Processing Data for Analytics. O’Reilly: Data Pipelines Pocket Reference[Book]
Raj, A., Bosch, J., Olsson, H. H. & Wang, T. J. (2020). Modelling Data Pipelines. Proceedings - 46th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2020, 13 20. https://doi.org/10.1109/SEAA51224.2020.00014
Gupta, S. (2020). Architecture for High-Throughput Low-Latency Big Data Pipeline on Cloud. Scalable Efficient Big Data Pipeline Architecture | Towards Data Science
Oleghe, O., & Salonitis, K. (2020). A framework for designing data pipelines for manufacturing systems. Procedia CIRP, 93, 724–729. https://doi.org/10.1016/j.procir.2020.04.016
Matskin, M., Tahmasebi, S., Layegh, A., Payberah, A. H., Thomas, A., Nikolov, N., & Roman, D. (2021). A Survey of Big Data Pipeline Orchestration Tools from the Perspective of the DataCloud Project *. (PDF) A Survey of Big Data Pipeline Orchestration Tools from the Perspective of the DataCloud Project *
