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, 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.
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 Speeds | The rate at which data is collected and processed |
Storage Speeds | The rate at which data can be stored |
Retrieval Speeds | The rate at which data can be retrieved from storage |
Error Rates | The frequency of errors in data processing (e.g., percent missing) |
Mean Time to Decision | The average amount of time it takes to make a decision based on data |
Source Performance Consistency | The consistency in data quality and output from different 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 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 Frequency | How frequently new code is deployed to production | |
Change Failure Rate | The percentage of changes that result in a failed deployment | |
Time To Restore Service | The average amount of time it takes to restore service after an incident | |
Customer Satisfaction Scores | Feedback from customers on their satisfaction with the product or service |
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:
Roles | Responsibilities | Skills | Tools |
DevOps Evangelist | Advocating and promoting DevOps culture and practices | Knowledge of Agile methodologies, DevOps principles, and communication skills | N/A |
Release Manager | Planning and coordinating software releases | Knowledge of release management processes and tools, project management skills | Jira, Git, Jenkins, CircleCI |
Automation Architect | Designing and implementing automation frameworks | Knowledge of automation tools and frameworks, programming skills | Selenium, Appium, Puppet, Ansible |
Experience Assurance Expert (XA) | Ensuring a positive user experience | Knowledge of user experience design, testing, and analysis | User testing tools, A/B testing tools |
Software Developer & Tester | Developing and testing software | Knowledge of software development and testing methodologies | Programming languages, testing tools |
Security & Compliance Engineer | Ensuring software security and compliance | Knowledge of security and compliance regulations and best practices | Security and compliance tools, vulnerability scanners |
Product Owner (PO) | Setting product vision and priorities | Knowledge of business strategy and product development | Agile frameworks, product management tools |
Utility Technology Player | Providing technical support and expertise | Knowledge of IT infrastructure and technical support | IT 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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