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.
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.
The main principles of DataOps are:
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, X 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.
The main principles of DataOps are:
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.
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.
And what about objectives? Those are different, too.
DataOps defines success through the speed, accuracy, and reliability of data flowing through the pipeline. That means benchmarking success against metrics like:
| Metric | Definition |
|---|---|
| Ingestion Speed | How quickly data is collected and brought into the pipeline |
| Storage Throughput | The rate at which data can be saved to storage systems |
| Retrieval Speed | How efficiently data can be accessed or fetched from storage |
| Data Error Rate | Frequency or percentage of errors encountered during data handling |
| Mean Time to Decision (MTTD) | Average time taken from receiving data to making a business decision |
| Source Quality Consistency | Consistency of data accuracy and reliability across data sources |
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:
| Metric | Definition |
|---|---|
| Availability | The percentage of time a system is accessible to users—often tracked with tools like Instatus. |
| Mean Time To Repair (MTTR) | Average time required to fix and resolve issues or incidents once detected. |
| Mean Time To Detect (MTTD) | Average time required to identify that a problem or incident has occurred. |
| Deployment Frequency | The rate at which new code or changes are deployed to production environments. |
| Change Failure Rate | Percentage of deployments or changes that result in degraded service, failure, or rollbacks. |
| Time To Restore Service | Average time it takes to restore normal operations after an unplanned service disruption. |
| Customer Satisfaction Scores | Measures of user or customer satisfaction with the product, service, or deployment outcomes. |
Both DataOps and DevOps require fairly specific team configurations in order to thrive.
DataOps teams are generally smaller than DevOps teams—only really requiring four roles to be filled:
| Roles | Responsibilities | Skills | Tools |
| Data Engineer | Creating and maintaining data lakes and warehouses | Databases, programming, and cloud infrastructure | SQL, Informatica, DataStage, SSIS, and Talend |
| Data Analyst | Visualizing and interpreting data | Programming, Statistics, ML, data cleaning, and data visualization | Excel, Looker, Tableau, Qlik View, and Altryx |
| Data Scientist | Creating algorithms & models | Data mining, ML, statistics, and programming | R, Python, SAS, and SPSS |
| DataOps Engineer | Designing and managing the data pipeline | DevOps, automation, cloud infrastructure, Agile, and process control | Python, shell scripts, and data test frameworks |
DataOps teams are most successful when they have a mix of technical and business knowledge.
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:
| Role | Responsibility | Key Skills | Common Tools |
|---|---|---|---|
| DevOps Evangelist | Drive adoption of DevOps culture and best practices | Agile, DevOps, leadership, communication | N/A |
| Release Manager | Plan, schedule, and oversee software releases | Release management, project coordination, CI/CD processes | Jira, Git, Jenkins, CircleCI |
| Automation Architect | Design and maintain automation strategies and frameworks | Scripting/programming, automation tools, infrastructure knowledge | Selenium, Appium, Puppet, Ansible |
| Experience Assurance Expert | Ensure software meets user experience expectations | UX design, QA/testing, analytics | A/B testing tools, user research tools |
| Software Developer & Tester | Write, test, and review code | Development best practices, test automation, debugging | Programming languages, test suites |
| Security & Compliance Engineer | Oversee product security and compliance with regulations | Security principles, regulatory standards, auditing | Security suites, compliance monitoring |
| Product Owner | Define product vision, roadmap, and priorities | Business analysis, product management, stakeholder engagement | Agile boards, product management tools |
| Utility Technology Specialist | Offer flexible technical support across domains | IT infrastructure, troubleshooting, operational support | Admin tools, monitoring & networking suites |
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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