The numerous departments, disciplines, and roles related to big data, IT operations, and software development may seem interchangeable to outsiders. But with all things data, the devil's in the details.
Case in point: DataOps and DevOps sound super-similar at first blush. They even have a shared heritage in agile development strategy. But each relates to distinct areas of high-quality software and application development. Here's a detailed breakdown to clarify those differences.
DataOps vs. DevOps: At a glance
DataOps (data operations) is a data management practice that aims to help organizations improve their data-driven strategies and processes. Roles in DataOps require knowledge of data analytics, data engineering, data governance, data science, and data quality.
DevOps (development operations) is a collaborative approach between data engineers and software developers that centers on app and software development as a whole. The goal of DevOps within an organization is to improve end-user customer experience through a combined focus on increased automation, faster deployment cycles, and superior data quality control.
Digging into DataOps: What’s it all about?
While popularized by Andy Palmer of Tamr and Steph Locke, Lenny Liebmann gets credit for coining the term "DataOps" in 2014 during his time as a contributing editor for Information Week.
As a discipline, DataOps evolved to bridge the gap between traditional DevOps practices and data engineering needs. Its goal is to help organizations streamline their data pipelines and ensure that they can quickly access, analyze, and securely act on their data.
Those working in DataOps (e.g. data scientists, data analysts) seek to achieve this by enabling and maintaining automated frameworks for managing the entire data lifecycle in a given business, from ingesting raw data all the way through mining data for insights.
On a good day, this helps organizations reduce complexity while also ensuring that their data is secure, reliable, and accessible when needed. However, with any aspect of business, DataOps comes with many challenges, arguably the most important being data quality control.
Organizations must ensure that their data is accurate, up-to-date, complete, and reliable before using it for analysis or decision-making. This requires establishing robust data governance processes, including data validation rules and periodic audits. However, successful data governance can't come at the expense of navigating the needs of performance optimization while staying a step ahead of ongoing security threats and malicious actors.
Further, data governance needs to be adaptive, ensuring that organizational changes are quickly addressed.
DataOps can help organizations tackle all of this by automating key processes and streamlining the implementation of data management best practices. This helps ensure the accuracy, completeness, and validity of your business's data while maintaining performance levels and safely securing data.
Digging into DevOps: What’s it all about?
Of the two, DevOps has the advantage over DataOps regarding which led to which. Coined in 2009 by author Patrick Debois, DevOps originally focused on discovering and implementing ways to increase the speed and efficiency of software development teams.
Overall, performance gains are often found through a focus on the collaboration between operative members and departments in a given production process. In software development, this typically includes collaboration between operations staff, software engineers, and other stakeholders in the IT department.
But there's no reason this collaborative emphasis couldn't be applicable to, say, a marketing team, as strategists, specialists, and managers work out the specifics of a social media proposal or campaign. This is why it’s now common to find DevOps methodologies applied to processes and projects outside the bounds of information technology.
Two DevOps processes, however, that remain specifically tied to software development are continuous integration (CI) and continuous development (CD).
CI is the process of testing code for errors or bugs before that code is deployed into production environments. This process allows DevOps teams to resolve potential conflicts early which, in turn, keeps the respective codebase stable during development.
As a complementary process, CD (sometimes referred to as continuous delivery/deployment, or CD2) involves making small, ongoing updates to software. Compared to older approaches where software would be entirely and intermittently overhauled, CD enables DevOps teams to deliver software to customers as soon as it’s tested and completed.
Implemented together, CI and CD form the basis for a development process called continuous software development, or CSD.
How to use DataOps
So, now that the differences between DataOps and DevOps are clear, how exactly can you leverage DataOps within your organization?
First, you need to define your data operation objectives. This involves understanding the performance and data usage metrics you’ll need to measure in order to achieve your unique strategic goals. Then, you can create a data architecture that supports these goals. This architecture may involve the use of access and control measures to protect sensitive information. Existing data sources may also need to be accounted for, and ultimately unified into a new and singular platform.
While creating your data architecture, you'll also likely need to set up data transformation pipelines that use extract, transform, and load (ETL) techniques, as these pipelines ensure data can be stored securely and transferred reliably between systems.
With architecture and pipelines in place, it’s time for process automation. This is an especially important step in your approach to DataOps, as automation helps prevent errors made when manual tasks are missed or forgotten. Automating key aspects of data management, like those that handle the flow of data between systems, increases overall data accuracy and reduces data latency.
Keep one thing in mind though: Automation never warrants ignorance. Teams still need to set up monitoring mechanisms for all aspects of their DataOps processes. Doing so ensures any issues that can still arise are quickly identified before they go on to cause serious damage, cost, or both.
Finally, it’s important to embrace the fact that DataOps isn’t a one-off consideration. Rather, DataOps is a continuous process within an organization, one that needs to be cultivated and maintained. This means ongoing training for everyone involved in the DataOps process, documentation of best practices, and ongoing analysis and process optimization. This keeps teams in top form, and it also helps an organization scale as its given marketplace evolves over time.
While this covers the basics of how to implement DataOps, you can take a deeper dive as needed with our recent article covering everything you need to know.
DataOps vs. DevOps: What next?
If you’re ready to tackle DataOps and/or DevOps within your own organization, we can help get your boat in the water. We’re a modern data orchestration platform for data team members to easily connect tools, automate workflows, and build a solid data infrastructure from day one.
We’ve built data products for some of the largest brands in business. So we’re well aware of the problems that come with implementing data at scale. That’s why observability and alerting are built into the Shipyard platform, ensuring that breakages are identified long before being discovered downstream.
Want to learn a little more? Take a moment to browse our ever-growing library of low-code Blueprints, or get started with our free Developer Plan today.
