In this special guest feature, Itamar Ben Hemo, CEO of Rivery, discusses commonalities and differences between DevOps and DataOps. Rivery is a DataOps platform that streamlines data integration, transformation, and orchestration. A seasoned business executive, Ben was co-founder and CEO of Vision.BI, a leading data consulting firm acquired by Keyrus Group. At Keyrus, he was founder and group vice president for North America.
Many assume, understandably, that DataOps is simply “DevOps for data.” Although the two frameworks have similar names, DevOps and DataOps are not the same methodology. However, the two frameworks do share many common principles. That’s why, for data professionals and other stakeholders, it’s important to understand what differentiates DataOps from DevOps. Read the comparison below to learn everything you need to know about DevOps versus DataOps.
DevOps Speeds Up the Delivery of High Quality Software Products
DevOps combines software development (Dev) and IT operations (Ops) to speed up the delivery of high quality software products. Originating in the late 2000s, DevOps was designed to break down the intra-company silos that cut off communication and coordination between the dev and IT teams. This lack of cohesion between dev and IT strained software deployment and led to delayed, inefficient, or broken software products.
By bringing these two departments under one umbrella, DevOps teams can write (dev) and deploy (IT) software within a unified, automated framework. DevOps introduced a set of core practices that are now standard in the software industry, including:
- Source code management, or version control, allows dev teams to track and control changes in source code, across different versions and time periods.
- Continuous integration (CI) integrates developer source code with a mainline code branch, preferably several times a day.
- Automated testing performs automated tests on new source code to provide the dev team with immediate feedback.
- Continuous delivery (CD) tests new code as a software artifact in a staging environment to ensure the quality and consistency of the product before going live.
- Continuous deployment (also CD) automatically pushes new code live into the production environment, ideally in small, frequent intervals.
In addition to these key components, DevOps frameworks also frequently incorporate agile development. Agile development focuses on small, incremental product updates as opposed to all-at-once releases. Agile breaks the software development process into “sprints,” each with targeted objectives for the dev team to complete. Lasting 1-4 weeks, sprints incorporate constant stakeholder feedback.
Since its inception a little over ten years ago, DevOps has rapidly revolutionized the way software is built and launched. Top DevOps teams now deploy software updates 208x more frequently than old-model dev teams, and maintain fail rates 7x lower. DevOps enables Amazon to deploy new code every 11.7 seconds, and Etsy to launch code updates over 60 times per day.
With such a successful track record, DevOps seems ripe for application to other areas of technology. And that is how DataOps first emerged. But DataOps is more than just DevOps for data.
DataOps Automates Data Orchestration to Quickly Deliver Data Across an Organization
DataOps is a methodology that combines technology, processes, principles, and personnel to automate data orchestration throughout an organization. Organizations use DataOps to deliver high-quality, on-demand data to institutional customers by speeding up the development and deployment of automated data workflows.
As organizations grow, and data demands become more complex, DataOps offers a flexible framework for delivering the right data, at the right time, to the right stakeholder. DataOps rapidly deploys new data infrastructure to meet the fast-changing priorities of all customers, from executives, to marketers, to SDRs.
DataOps adopts many of the same principles as DevOps. But while DevOps automates software deployment, DataOps automates data orchestration – the end-to-end delivery of data from source to target. DevOps builds software products; DataOps builds data workflows. These data workflows automate data ingestion, transformation, and orchestration. DataOps uses data infrastructure, such as data pipelines and SQL-based transformations, to power these automated workflows.
The “software product” that DataOps teams work on during sprints is typically data infrastructure. In some cases, the infrastructure is actually treated as code, or infrastructure as code (IaC). Within this framework, DataOps teams can apply the principles of DevOps to IaC as if it were any other software product. However, other teams prefer to construct data infrastructure through data management platforms and user interfaces, but still apply the principles of DevOps during the building process.
Here’s how some of the core components of DevOps, listed in the section above, apply to DataOps:
- Version control allows dev teams to track and control changes in data infrastructure, across different versions and time periods. This streamlines revision, reversion, and debugging of IaC.
- Continuous integration (CI) integrates developer IaC with a mainline code branch, preferably several times a day. With CI, developers never deviate too far from the main code branch.
- Automated testing performs automated tests on new data infrastructure to provide the dev team with immediate feedback, including unit tests, functional tests, and end-to-end tests.
- Continuous delivery (CD) tests new data infrastructure in a staging environment to ensure the quality and consistency before going live. This helps avoid bugs and disruptions for users.
- Continuous deployment (also CD) automatically pushes new data infrastructure live, into the production environment, ideally in small, frequent changes. This removes manual code and pipeline merging tasks and accelerates product updates.
DataOps and DevOps may share the same principles of development and deployment. But DevOps is only one component of DataOps. Personnel, technologies, and agile processes also play critical roles in DataOps.
Organizations can only realize the full benefits of DataOps when all these components are combined into a unified framework. While the two methodologies overlap, and share very similar names, they are not interchangeable. Data teams must understand the differences between them to build effective DataOps frameworks.
DataOps Harnesses the Best of DevOps, But They Are Not the Same
As the economy becomes even more data-driven, and the data needs of institutional customers quickly grow, data teams cannot simply rely on technology to remain competitive. They must build agile, organizational frameworks to deliver data when and how stakeholders require it. This is what DataOps is designed to do. By harnessing DevOps, DataOps injects the top methods and practices of software development into the data orchestration process, enabling the speedy delivery of data to all organizational customers.
If there’s one key takeaway from this article, it’s this: DataOps needs the principles of DevOps to provide stakeholders with data quickly and efficiently, but DevOps and DataOps are not the same thing.
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