To determine which approach is right for your business, you may want to consider factors such as your team’s existing skillset, your organizational culture, and your budget. Ultimately, the most important thing is to choose an approach that suits your individual needs and supports your larger business goals.
DataOps and DevOps are two approaches to improving the development of data products and software, respectively. While there are many similarities between these two approaches, there are also some key differences. For example, DataOps requires different tools than DevOps, and places a greater emphasis on collaboration and continuous improvement.
While there are many similar objectives between DataOps and DevOps, such as improving the speed and quality of development, they have some key differences. For example, while both approaches require a strong focus on automation, DataOps requires additional tools to improve data quality and completeness.
What are the similarities between DataOps and DevOps?
Many of the methods that enable DataOps have been derived from the same origins as DevOps. Similarly, just as organizations require DevOps in order to provide a high-quality, consistent platform for software and feature development, data firms rely on these same characteristics to realize fast data engineering and analysis creation. It’s relatively simple to use DataOps with an established DevOps architecture. With DataOps, several key DevOps ideas have been introduced, including:
- Agile development
- Focus on delivering business value
- Continuous integration and continuous delivery (CI/CD)
- Automated testing and code promotion
- Reuse and automation
The Differences
Despite the parallels between DevOps and DataOps, there are several significant distinctions.
The human factor: DataOps and DevOps are used by individuals with different personalities and skill sets. DataOps participants might be tech-savvy, but their understanding is typically theoretical. Data engineers, data scientists and analysts who focus on developing models and visual aids may be part of the DataOps team. Although software developers and engineers are included in the DevOps group, they might not have the same level of expertise as data professionals.
The culture: The collaborative and communicative environment that is essential to DevOps can be challenging for those who prefer to work independently. DataOps relies on team members who are willing to share their knowledge so that processes can be automated and streamlined.
The tools: DataOps requires different tools than DevOps. DataOps might need data management platforms, data quality assessment tools and data discovery platforms that aren’t necessary for DevOps.
The process: The application delivery process for software is different from the process of developing and deploying data products. In general, software can be delivered more quickly than data products. The two life cycles of DataOps and DevOps do have some comparable interactive properties. However, the former differs in that it includes a data pipeline as well as an analytics construction process that are both operational and interrelated. While conceptually, the DataOps pipelines resemble those of DevOps development processes, experts frequently observe that they are more difficult.
The focus: DataOps focuses specifically on developing, deploying and managing data products. DevOps also encompasses the software delivery process, but might not involve as much of a focus on data products.
Orchestration: In the DevOps process, application code does not need as much orchestration. DataOps, on the other hand, necessitates both the data pipeline and analytics development orchestration. Although orchestrating data flows is a common occurrence in DataOps pipelines, there is rarely any coordination between application engineering and DevOps processes.
Testing: The DataOps pipelines are, in fact, distinct from the DevOps pipeline; testing occurs throughout both the data and analytical development processes in DataOps. These exams look for abnormalities, flag aberrant data values, and—unlike DevOps—verify new analytics before deployment. The same is true for these tests, which are now incorporated into a production workflow.
The result: DataOps seeks to achieve a state of data product development that is both rapid and robust. The goal is for analytics to be up-to-date, accurate, and useful to the organization. As such, DataOps might incorporate features that streamline workflows, improve collaboration, or manage costs. DevOps, on the other hand, focuses on delivering software quickly and efficiently.
Test data management: In most DevOps environments, test data management hardly takes priority; with DataOps, it’s vital to accelerate analytics development so that innovation keeps pace with agile iterations.
Tools: DevOps is the ‘father’ of DataOps, and as such, the tools needed to support the latter are still in their infancy. While testing in DevOps is primarily automated, DataOps doesn’t have the same luxury – most users modify testing automation tools or build their own from scratch.
Exploratory environment management: Generally, data teams use more tools than software development teams. Moreover, exploratory environments in data analytics are more challenging from a tools and data perspective; data teams also naturally depart from data islands across the enterprise.
While foundationally, the concepts of DevOps serve as a starting point for DataOps, the latter involves additional considerations to maximize efficiency when operating data and analytical products. Nevertheless, both serve their intended audiences, reducing data debt and evolving data products or shortening systems development life cycles or providing continuous delivery. For businesses looking to make internal data-related processes more efficient, they should start by examining best practices associated with DevOps.
Now that we’ve explored the similarities and differences between DataOps and DevOps, it’s time to answer the question: which one is right for your business?
The answer, of course, depends on your specific needs and goals. If you’re looking to streamline your data product development process, then DataOps is the way to go. If you’re more interested in delivering software quickly and efficiently, then DevOps is the better choice.
Of course, there’s no reason you can’t use both DataOps and DevOps in conjunction. In fact, many companies find that a combination of the two is the best way to improve their data and software development processes.
In conclusion, there are several key differences between DataOps and DevOps. These include the focus, tools, culture, and process used by each approach.
