What’s the difference between DataOps and DevOps?
If you’re looking to improve the agility of your data projects, then the DataOps methodology might just be for you.
But, before we dip into the nuances of DataOps, it’s crucial you don’t fall foul of a big mistake that many organizations make. If you do, you’ll not only fail to deliver on the benefits of DataOps, you’ll also add needless confusion and complexity to your operations.
What is that mistake, you ask?
Well, it’s thinking that DataOps and DevOps are the same thing. When in reality, they’re very different.
When organizations who have previously used DevOps start applying DataOps, they tend to think they’re the same thing. This is often driven by those who have experience with DevOps. They may believe that they can take everything they’ve learned from DevOps and apply it to data, and then have ‘DataOps’.
In this blog, we’re going to explore the differences between DataOps and DevOps so that you’re clear going forward.
Defining DevOps and DataOps
The truth is, while there are similarities between DevOps and DataOps, they’re actually very different. Let’s first unpack a definition of each.
Here’s a definition of DevOps:
DevOps involves continuous development of software. It tries to reduce the development lifecycle through continuous delivery. DevOps is complementary with the agile development methodology, and by improving the delivery time, DevOps increases time to value. This makes for more profitable and rapid software development programs.
And here’s a definition of DataOps:
DataOps involves continuous improvement of data quality and reduction in cycle time of data analytics. It uses the agile methodology but it isn’t tied to using any specific software. Having matured over the last decade, the DataOps methodology is a new and rapidly growing approach to data analytics.
If you’d like to learn more, you can read our complete guide to DataOps here.
Key differences to consider
Having defined both DevOps and DataOps, you’ll no doubt be starting to see the difference.
But it’s worth taking the time to look a little closer and examine some of the specific differences that set them apart.
So, in no particular order, here are some of the more specific, nuanced differences between DevOps and DataOps.
- With DevOps, it’s the code that’s the important thing. But with DataOps, data is the important aspect.
- With DevOps, the majority of work is done by engineers who are involved in developing the code. However, with DataOps, it’s the end-user who gets value from the data.
- DevOps involves mostly technical people. But with Dataops, you involve business users and stakeholders too. And they aren't thinking in code and tech terms – they're more concerned with business outcomes.
- Continuous inspection of data is key with DataOps but doesn’t come into play with DevOps. The question with DataOps that consistently arises is: ‘How do I know the data in the pipeline isn’t faulty or tainted?’
- DevOps requires fairly limited coordination – you just need the team of developers to work together. However, DataOps requires the consistent coordination of changing data (and everyone who works with it) across the entire organization.
Another way to look at it is to consider that, with DevOps, the software itself is what’s important – so, if you were developing Adobe Photoshop, for example, that would be the product.
On the other hand, when you’re working with DataOps, you’re selling the ‘flow’ of data that’s within the software. It’s not about selling the software itself.
In this way, DataOps can be thought of as being more than DevOps because it encompasses both the software and the data.
Make your DataOps initiative a success
Another mistake organizations tend to make (as well as confusing DataOps with DevOps) is trying to do DataOps without putting the right data platform in place.Why CloverDX Should Be Part Of Your DataOps Toolkit
After all, the many benefits of DataOps, such as continuous delivery and faster resolution of problems, require tools that empower you to handle your data effectively at scale.
When you use a tool like CloverDX, you’ll benefit from DataOps-driven automation, making it easier to accelerate your data analytics and bridge the communication gap between business and technical teams.
If you’d like to find out more about how CloverDX follows the DataOps methodology, and how it could assist your organization, get in touch with us here.