DataOps and DevOps are two distinctly different pursuits. Both are based on agile frameworks that are designed to accelerate working cycles. But where DevOps focuses on product development, DataOps aims to reduce the time from data need to data success. At its best, DataOps shortens the cycle time for analytics and aligns with business goals.

When DataOps is successful, organizations can realize immense improvements in how they find, use, and extract value from their data.

What is DevOps?

Development Operations, or DevOps, combines the engineering side of product development with the operational side of product delivery. DevOps was initially created to respond to the rapid pace at which Google, Facebook, and other SaaS companies developed and introduced new products.

In software development, and with today’s modern, agile approaches, DevOps is a continuous, looping process:

  • Development plans, creates, and packages software for delivery in a continuous delivery and build lifecycle.
  • Operations then releases and monitors the products.

When new or additional development is needed, Operations feeds information to Development, which then plans its creation. And so it goes.

DevOps brings together different teams to reduce the cost of product development and increase the speed of release cycles. Removing the silos separating Engineering, IT Operations, Software Development, Quality Assurance, and other teams can also increase scale, improve security and reliability, and enable faster, more efficient innovation. The DevOps methodology has changed how modern organizations deliver software, reducing months-long delivery cycles down to just minutes.

What is DataOps?

Data Operations, or DataOps, is like DevOps in that both are based in agile, continuous improvement thinking. And while DataOps has a similar methodology to DevOps, its goals are distinct. DataOps is designed to build high quality data and analytics solutions at an increasingly accelerated pace, and with higher reliability, as time goes on.

As organizations have struggled beneath a deluge of data, their data teams faced growing expectations that the business put that data to work. Data teams were inspired by the DevOps methodology to create DataOps.

DataOps was created to leverage the underlying manufacturing methodologies of lean manufacturing, statistical process control, and, of course, agile development.

DataOps seeks to quickly find the right data for the right application. It brings together business users, data scientists, data analysts, IT, and application developers to fulfill the business need for insights. DataOps then works to continuously improve and adjust data models, visualizations, reports, and dashboards to achieve business goals.

DataOps fosters cross-functional collaboration and automation to build fast, trustworthy data pipelines so your business can wring the most value from your data.

The Agile Connection

In looking at DataOps vs DevOps, one similarity is the use of agile cycles. Agile project management takes a continuous, iterative approach that results in faster delivery but in smaller increments. Instead of working for months or years on a massive but singular deliverable, in an agile cycle, teams deliver small bits of information that build on each other as they go.

Agile management reduces the time data teams spend troubleshooting bugs and errors. Agile allows for frequent feedback and the ability to shift tactics on the fly so mistakes, bugs, or misdirections are caught before they consume too much effort or require a massive effort to fix.

Data governance is crucial for effective DataOps. Active, non-invasive governance supports continuous improvement and ensures only high quality data is fed into the DataOps system on the front end.

The difference between DataOps vs DevOps use of agile comes back to the product delivered:

  • The DevOps methodology begins with a (relatively) static product and delivers an improved version of that (relatively) static product and user base.
  • The DataOps methodology, conversely, begins with a fluid and constantly changing set of data and data sources, and seeks to address a fluid and constantly changing set of business needs, stakeholders, users, and goals.

DataOps vs. DevOps: Main Differences

DataOps and DevOps share many similarities, yet the fundamental products they deliver are decidedly different. Where DevOps engineers, develops, and delivers software applications, DataOps builds, tests, and releases data products.

Data is different from software, obviously, so each discipline requires a different set of skills and team collaboration to be effective.

DataOps vs. DevOps: Skill & Team Requirements

DataOps vs. DevOps: Skill & Team Requirements

The delivery pipelines for DataOps vs DevOps are also distinct. But the general delivery cycles are similar, and can be understood in three phases:

  • Build, with a focus on speed
  • Test, to ensure quality
  • Release, to enable flexibility

In the DevOps process, the software application or specific capabilities are built, tested, and released. That’s a simplification, but it’s a common process and well understood.

DataOps, however, requires additional steps before and after the build, test, and release stages to ensure the proper data is captured and users apply the data appropriately.

DataOps vs. DevOps: Pipeline Comparison

DataOps vs. DevOps: Pipeline Comparison

Other differences between DataOps vs DevOps are the ultimate goals of the different components of the development cycles. For example, though both DataOps and DevOps have a quality component, DevOps aims to create a quality end product, while DataOps must ensure high quality data enters the process and trusted, high quality outputs are used appropriately by the business. Other nuanced differences between DataOps vs DevOps are:

  • Quality: DataOps ensures usage of high quality data for high quality outputs; DevOps delivers a quality product.
  • Collaboration: DataOps works with business users, application developers, and IT operations; DevOps works with engineering and development teams.
  • Cycle Times: DataOps strives to build a continuous data pipeline so business users become self-sufficient; DevOps strives for shorter release cycles to meet business demands.
  • Operations: DataOps is constantly addressing new and changing data challenges involving many sources and needs; DevOps runs repeatable, highly similar cycles.

Using DataOps to Empower Users

DataOps helps to foster businesswide collaboration by deploying tested, trusted, and monitored data solutions to create data pipelines that empower business users. It eliminates silos between your data consumers and IT to foster a Data Culture throughout your organization.

Learn how Alation can help you build faster, higher quality data solutions through DataOps.

Alation state of data culture report q1 2021