DataOps vs. DevOps: Everything You Need To Know

DataOps and DevOps share many similarities—a focus on automation, cross-functional teams, a reliance on Agile methodologies… the list goes on.

There’s a good reason for this overlap. DataOps combines DevOps principles with data management best practices to enable the rapid deployment and optimization of business-critical data pipelines.

So, while the two share many similarities, there are also some key differences—and in this Instatus guide, we’ll be walking you through them. Here’s everything you need to know about the relationship between DataOps and DevOps.

What is DataOps?

DataOps is part development approach, part data engineering process, and part operations methodology.

It’s a practice of collaboration between the development, operations, and data teams to automate the delivery of data-driven applications that advance business goals. DataOps focuses on bringing together these three functions in order to increase agility, speed up time to market, and foster better communication among various stakeholders.

Principles of DataOps

The main principles of DataOps are:

  1. Collaboration: DataOps teams are cross-functional—including data scientists and software engineers.
  2. Automation: DataOps teams should strive to automate as much of the process as possible, including data pipelines, processing, and security protocols.
  3. Version Control: DataOps teams should use version control to ensure that changes made to the application are tracked and can be reverted if needed.
  4. Data Quality & Governance: DataOps teams should be aware of the importance of data quality and governance throughout their application lifecycle.
  5. Continuous Learning & Improvement: DataOps teams should constantly strive to learn and improve their processes in order to keep up with new developments within the industry or within a company.

Pros & Cons


  • End-to-End Efficiency: DataOps enables teams to achieve end-to-end efficiency. By automating processes, data delivery is faster and more accurate, allowing teams to focus on value-added activities.
  • Improved Collaboration: DataOps encourages collaboration between the data, development, and operations teams to create a unified approach to data-driven applications.
  • Self-Service Data Marketplace: DataOps allows teams to create a self-service data marketplace where users can access and share clean, reliable data with ease.


  • A Lack of Clarity Around What DataOps Entails: DataOps is a relatively new concept, and many teams lack clarity about what it entails. This can lead to confusion and miscommunications when trying to implement DataOps initiatives.
  • High Investment in Technology: DataOps requires a high investment in technology, such as automation tools and monitoring solutions. This can be a barrier to entry for many organizations.
  • A Lack of Data Fundamentals: DataOps requires a strong understanding of data fundamentals, such as data quality, governance, and security. Without this understanding, teams may struggle to implement DataOps initiatives effectively

What is DevOps?

We've written quite a bit about DevOps on the blog, but here's a quick refresher.DevOps is an approach to software development that emphasizes collaboration between the development (Dev) and operations (Ops) teams. It focuses on using automation tools to streamline processes, promote continuous integration and delivery, and increase agility.The result of DevOps (at its best) is an interconnected software stack that handles 90% of your busy work without the need for human intervention.

Take Instatus, for example—our status page builder integrates with a ton of monitoring tools and communication platforms. When issues occur, Instatus can automatically send notifications and update your status page, Slack channels, Intercom page, Twitter feed, and more. This ensures that your support teams won’t be flooded with tickets and that your development teams can develop a solution without excess interruption.

Principles of DevOps

The main principles of DataOps are:

  1. Collaboration: DevOps teams should be cross-functional, including software engineers and ITOps personnel.
  2. Automation: DevOps teams should strive to automate as much of the process as possible, from development to deployment.
  3. Continuous Integration & Delivery: DevOps teams should use continuous integration and delivery tools to speed up the application development process.
  4. Version Control: DevOps teams should use version control to ensure that changes made to the software are tracked and can be reverted if needed.
  5. Monitoring & Observability: DevOps teams should use monitoring and observability tools like Instatus to gather insights into the performance of their applications in production.

Pros & Cons


  • Faster Time to Market: DevOps helps teams get applications to market faster, as processes are automated and streamlined.
  • Reduced Risk: By focusing on continuous integration and delivery, DevOps reduces the risk of errors during development and deployment.
  • Improved Quality: Through automated testing, DevOps helps ensure higher-quality applications.


  • Lack of Meaningful Metrics: Without the right metrics, it can be hard to track progress and measure success.
  • Added Complexity: DevOps can add complexity to the application development process, as teams need to be familiar with a variety of tools and technologies.
  • Unrealistic Goals & Expectations: DevOps can be difficult to implement, and teams may set unrealistic goals or expectations for the process. This can lead to frustration and delays.

4 Key Differences Between DataOps and DevOps

1. Meaning

The most obvious difference between DataOps and DevOps is meaning—these are two different (albeit related) concepts that have different goals.DataOps is concerned with using Agile methodologies and DevOps principles to streamline the data pipeline, while DevOps focuses on streamlining application development. In cases where application development requires input data, there’s bound to be overlap. But still, the main focus of DataOps is on data, and the main focus of DevOps is on applications.

2. Goals

The goals of DataOps and DevOps are also different from one another. The goal of DataOps is to create a streamlined data pipeline that allows the business to make smarter, more impactful decisions in less time. DevOps, on the other hand, focuses on automating the application development process to help speed up delivery times and create a more consistent product for end users.

3. Objectives & Success Metrics

And what about objectives? Those are different, too.

DataOps Objectives & Success Metrics

DataOps defines success through the speed, accuracy, and reliability of data flowing through the pipeline. That means benchmarking success against metrics like:

Ingestion SpeedsThe rate at which data is collected and processed
Storage SpeedsThe rate at which data can be stored
Retrieval SpeedsThe rate at which data can be retrieved from storage
Error RatesThe frequency of errors in data processing (e.g., percent missing)
Mean Time to DecisionThe average amount of time it takes to make a decision based on data
Source Performance ConsistencyThe consistency in data quality and output from different sources

DevOps Objectives & Success Metrics

DevOps objectives are related to creating a better end-user experience—reducing manual errors in development processes, automating tasks, shortening delivery times, and making applications more reliable. Common success metrics include:

AvailabilityThe percentage of time that a system or application is available to users—usually reported by a tool like Instatus
Mean Time To Repair (MTTR)The average amount of time it takes to resolve an issue or incident
Mean Time To Detect (MTTD)The average amount of time it takes to notice an issue or incident
Deployment FrequencyHow frequently new code is deployed to production
Change Failure RateThe percentage of changes that result in a failed deployment
Time To Restore ServiceThe average amount of time it takes to restore service after an incident
Customer Satisfaction ScoresFeedback from customers on their satisfaction with the product or service

4. Team Requirements

Both DataOps and DevOps require fairly specific team configurations in order to thrive.

DataOps Teams

DataOps teams are generally smaller than DevOps teams—only really requiring four roles to be filled:

Data EngineerCreating and maintaining data lakes and warehousesDatabases, programming, and cloud infrastructureSQL, Informatica, DataStage, SSIS, and Talend
Data AnalystVisualizing and interpreting dataProgramming, Statistics, ML, data cleaning, and data visualizationExcel, Looker, Tableau, Qlik View, and Altryx
Data ScientistCreating algorithms & modelsData mining, ML, statistics, and programmingR, Python, SAS, and SPSS
DataOps EngineerDesigning and managing the data pipelineDevOps, automation, cloud infrastructure, Agile, and process controlPython, shell scripts, and data test frameworks

DataOps teams are most successful when they have a mix of technical and business knowledge.

DevOps Teams

DevOps teams benefit from cross-functional skill sets like software development, IT operations experience, quality assurance, testing, security compliance, and more. Usually, this means a larger team is required:

DevOps EvangelistAdvocating and promoting DevOps culture and practicesKnowledge of Agile methodologies, DevOps principles, and communication skillsN/A
Release ManagerPlanning and coordinating software releasesKnowledge of release management processes and tools, project management skillsJira, Git, Jenkins, CircleCI
Automation ArchitectDesigning and implementing automation frameworksKnowledge of automation tools and frameworks, programming skillsSelenium, Appium, Puppet, Ansible
Experience Assurance Expert (XA)Ensuring a positive user experienceKnowledge of user experience design, testing, and analysisUser testing tools, A/B testing tools
Software Developer & TesterDeveloping and testing softwareKnowledge of software development and testing methodologiesProgramming languages, testing tools
Security & Compliance EngineerEnsuring software security and complianceKnowledge of security and compliance regulations and best practicesSecurity and compliance tools, vulnerability scanners
Product Owner (PO)Setting product vision and prioritiesKnowledge of business strategy and product developmentAgile frameworks, product management tools
Utility Technology PlayerProviding technical support and expertiseKnowledge of IT infrastructure and technical supportIT support tools, networking tools

Want to learn more? Check out our guide to key DevOps roles.


DataOps and DevOps are two modern approaches to software development and delivery that are revolutionizing the way businesses deliver products. Each approach has its own set of roles, responsibilities, and technologies to ensure successful product delivery. At Instatus, we support DevOps and DataOps teams with beautiful, interactive status pages and powerful integrations—all fully operational in minutes, not hours. Monitoring your services and updating stakeholders has never been easier!Ready to get more out of your status page? Sign up for a free account today.

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