DataOps vs. DevOps: What's the Difference?

Stephen Roddewig
Stephen Roddewig


The tech sector has a special appreciation for acronyms and abbreviations. DRY, DevSecOps, YAML, SQL, and MVP are all examples.

dataops vs devops

DataOps and DevOps are two abbreviations that often get confused. While they both are rooted in agile development strategies, they are focused on different business areas: data pipelines for DataOps and software development for DevOps. The following sections will review the common ground and distinctions between each method.

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Let's start by defining these methods.

What is DataOps?

DataOps is an approach to data analytics and data-driven decision making that follows the agile development methodology of continuous improvement. The goal of DataOps is to reduce the cost of data management, improve data quality, and deliver insights to analysts and business users faster by creating data pipelines.

DataOps diagram showing the pipeline that source data must travel through before reaching business users

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DataOps is achieved through several methods, including automating manual data collection and analysis methods, increasing reusability, democratizing access to data, and continuously monitoring the data pipeline. Data teams implement the practice to streamline the development of data-driven applications and enhance the quality of data available through advanced analytics, visualizations, reporting, and dashboards.

DataOps connects stakeholders from across the organization with data engineers, data scientists, analysts, and IT to determine which metrics are valuable for creating business intelligence. The DataOps team then determines which data sets are relevant, which technologies can extract the data, and how to transform and analyze the retrieved data. This collaboration with business users is also crucial to determining if the finished product provides the insights they need.

Now that you understand DataOps, let's define DevOps.

What is DevOps?

DevOps is an approach to software development that aims to remove roadblocks and achieve a continuous cycle of iteration and improvement.

This means that DevOps closely parallels the agile development methodology, but DevOps goes a step beyond the traditional development team by incorporating the IT operations and quality assurance teams into the process. Now, the development team isn't solely concerned with delivering the product but how it performs after deployment as well (AKA operations).

DevOps diagram showing the software development lifecycle between the development and operations teams as a figure eight loop

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The main goal of DevOps is to improve collaboration and reduce friction in the development process. In other words, DevOps is centered on efficiency. Its benefits include increased communication between the product teams, cost savings, constant improvement, and quick implementation of customer feedback.

After reviewing the definitions of DataOps and DevOps, you might conclude the only common ground between these two concepts is that both end in "Ops." However, they share a framework and follow similar approaches to achieving their goals. Let's examine these similarities in more detail.

DataOps vs. DevOps: The Similarities

DataOps and DevOps both have roots in agile project management. The following sections expand on this shared background.

DataOps versus DevOps chart showing agile methodology, value through iterative cycles, and increased collaboration similarities

Agile Methodology

DevOps and DataOps are extensions of the agile development methodology to their respective domains: software development and data analysis. Agile emphasizes flexible thinking and quick adaptations to meet changing business needs and take advantage of new technologies and opportunities. DevOps and DataOps follow this philosophy to optimize their pipelines.

Value Through Iterative Cycles

Both methods use short iterative cycles to quickly produce results and collect feedback from stakeholders to inform their next steps. Incremental development allows users to take advantage of the deliverable sooner and determine whether it meets basic needs. DevOps or DataOps teams can then start building the next layers of the product or change course as needed.

Increased Collaboration

DataOps and DevOps are built on breaking down silos between teams. In DataOps, data engineers and scientists work with business users and analysts to develop insights that are valuable to business goals. In DevOps, development, operations, and quality assurance teams collaborate to produce better software for customers. Both models prioritize feedback from the end user as a key performance indicator.

Now that you understand their similarities, let's review the differences between DataOps and DevOps.

DataOps vs. DevOps: The Differences

DataOps and DevOps share a common framework and approaches. However, their outcomes are distinct, and their processes differ when it comes to workflow, testing, and feedback. The following sections expand on these distinctions.

DataOps versus DevOps chart showing outcome, workflow, testing, and feedback differences


The largest difference between these two approaches is their outcomes. DataOps is focused on creating data streams that constantly deliver data and produce information for end users. This process may involve building applications for transforming and displaying data or may focus more on optimizing existing infrastructure.

DevOps is focused on quickly delivering valuable software to customers through fast deployments and iterative improvements that are guided by customer feedback. This process involves delivering a minimally viable product (MVP) as soon as possible and expanding its functionality over subsequent development cycles.


DataOps is used to stream data to those who need it to inform their decisions. This means that the DataOps team is constantly working to ensure the pipeline is still delivering the best data. Data sets can change and expand over time, so consistent monitoring and improvement of existing infrastructure are equally as important as building pipelines for new use cases.

DevOps is also meant to move quickly, but each piece of the DevOps pipeline still happens in defined stages. Some organizations may be releasing new features on a daily or hourly basis with DevOps and CI/CD, but even they are not moving at the speed of a DataOps pipeline. The latter must move and transform new data as soon as it's collected, which could mean multiple deliveries a second, depending on the data volume.


In DataOps, the true statistic isn't always known, so test results need to be validated for correctness. Does the outcome factor in all available data that's relevant? Does it use the most current data? A DataOps team may ask these questions of their test results — and more to fit their use case — to ensure analysts can have confidence in the information presented.

In DevOps, outcomes are defined and expected, so the testing phase is straightforward. Does the application deliver the desired result? If it does, then move on to the next text. If not, debug and test again.


Both models value feedback from the customer. However, DataOps places a higher emphasis on feedback from business users and analysts to determine if the deliverable meets their needs. These stakeholders typically have more context on the business processes that produce the data and the decisions they will make based on the information provided.

By contrast, DevOps does not necessarily require feedback from the customer unless an aspect of the application is not meeting their needs. If the end user is happy with the delivered product, then their feedback is voluntary. That said, teams should still monitor application usage and other DevOps metrics to determine if their product is truly satisfying all use cases or if there are areas for improvement.

The differences between DataOps and DevOps are summarized below.

  DataOps DevOps
Outcome Focuses on building data pipelines to deliver business intelligence to end users. Focuses on building software applications to deliver new functionality to end users.
Workflow Continuous monitoring of data pipelines is needed to ensure accuracy of data at any given time in addition to building new pipelines. Defined development stages for application features with frequent releases and deployments.
Testing Metric may not be known prior to data transformation and analysis, requiring additional validation beyond normal testing to ensure information is accurate. Outcome is defined in advance, so the testing process is a straightforward evaluation of whether the application behaves as expected.
Feedback Requires feedback from the end user to validate the solution delivered meets expectations and provides the best information. Values feedback from the end user but does not require it if the customer is satisfied with the solution delivered.

If you want to learn more, check out his video from Anant for an in-depth examination of the differences between DataOps and DevOps:

DataOps and DevOps together enable agile organizations.

DataOps and DevOps are complementary approaches to creating a more responsive business — not interchangeable terms.

Together, these concepts build and streamline the development and data pipelines to deliver valuable software and insights to the end user faster. By following the agile method of constant collaboration, incremental improvement, and a focus on feedback, they support your internal teams and your customers.

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