The Differences of DevOps and DataOps


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How different are DevOps and DataOps? Are we splitting hairs or hairstyles? Both are aligned to Agile principles and have a number of processes in common, but they have some key differences, too. Both are also very complex and aim to solve mostly different sets of challenges while also working to improve team cross-functionality. So, let’s take a look to get a better understanding of both.

What is DevOps?

DevOps covers a tremendous amount of ground as it’s relevant to the entire Software Development Lifecycle (SDLC). As we’ve defined it previously,

DevOps entails Agile practices, automated tools, and the cultivation of a team mindset to synthesize software development, quality assurance, and operations into increasingly cross-functional teams to increase the speed, efficiency, and quality of software development relative to both team and software business objectives.

Complex, yes. If we were to simplify, “DevOps is a mindset to continuously improve everything about how everyone does software development.” The faster and better we can develop software, the more profitable our teams will be, as we discussed in How Will AI Transform DevOps?

Even Puppet, which produces the annual State of DevOps reports, acknowledges that DevOps is still somewhat nebulous. The case can also be made that DevOps has a vested interest in activities that take place even before the SDLC begins – where the hiring or outsourcing of developers is concerned with developer skill has a major impact on the SDLC. And, it can also be said that DevOps’ goal of cultivating cross-functional teams readily lends to DevOps being an implicit element of nearly every job and role.

Continuous Integration, Delivery, and Deployment are DevOps practices that significantly reflect upon the maturity of DevOps on a team and company level. Each extends the range of automation across the SDLC to reduce the need for human intervention (i.e. merge approvals). The more a process depends upon humans to do it, the more inefficient it is likely to be.

GoogleTrends DevOps 5-Year Topic Trend (Worldwide)

What is DataOps?

A relatively new practice, DataOps has emerged from a set of best practices to become a discipline in its own right beginning around 2014 but has really picked up steam since mid-2018. DataOps is very different from DevOps even though it has several things in common. We see the definition of DataOps as:

DataOps entails Agile practices, organizational structure, statistical process control, and automated tools to continuously improve the quality and speed of the entire data lifecycle to align with business goals, improve operational efficiency, and facilitate better decision-making.

Or, to simplify, DataOps is about continuously improving the value of the data we use. That value includes issues like quality (accuracy and completeness) and is roughly proportionate to how fast we can:

  • a) see events,
  • b) properly analyze them,
  • c) reach decisions – and
  • d) take action:

Relationship of Data Value vs Time

Like we noted previously in discussing modern data analytics, DataOps has two major areas of effort:
  • Automation of the efforts that go into collecting, storing, integrating, organizing, cleaning, validating, and analyzing data.
  • Cultivating the skills, habits, processes, and culture so people can easily use this data to find actionable insights, and implement them.
Statistical Process Control also plays an important part in monitoring and controlling the data analytics pipeline to continuously verify that it is working. The goal is to use real-time data to drive business decisions. How this is defined can vary considerably – by day, hour, minute, and in stocks trading (NYSE and NASDAQ), even by the nanosecond.

GoogleTrends DataOps 5-Year Topic Trend (Worldwide)

GoogleTrends DataOps Topic Interest in the United States

Key Differences and Pipeline Comparison

DevOps and DataOps share many of the same principles and processes, but also have some key differences. For starters, it’s nigh impossible to practice DataOps without automation. While far from optimal, you don’t necessarily need automation to practice DevOps. It’s far easier to build teamwork than it is for a team to manually process Big Data.

Plus, where DevOps testing focuses almost entirely upon code, DataOps needs to test code and data in equal proportion. Just as a single typo can throw your software out of whack, it can sabotage an entire data library leading to erroneous reports fueling bad decisions.

Comparison of DevOps and DataOps Processes

The simplicity of this process belies that DataOps can involve 18 or more distinct steps:











Much of the additional complexity involved with DataOps is wrapped around two components:

Data Sandbox Management involves defining the data sources, tools, services, and vendors are to be used for a DataOps project. It’s similar to how developers set up their own isolated environments based upon project specifications for selecting software languages, libraries, tools, and services. But where developers tend to use a few dozen components, DataOps may need to synthesize hundreds of data sources using a wide variety of tools and services.

Data Orchestration is the automated process of bringing all of this data together from so many different sources and making it more useful so people can use it. This would be impossible without automation to address the challenges associated with the Voluminous V’s of Big Data (Volume, Velocity, Variety, et al).

These sources often involve third-party APIs, but could also entail a variety of platforms, periodicals, and reports using a very wide range of electronic file formats. Not all DataOps projects are so verbose – but data collection efforts tend to expand over time.

Five Key Issues DataOps Seeks to Solve

Have you checked out Apple TV+’s Foundation? It’s based on a series of stories by science fiction writer, Isaac Asimov. In it, Hari Seldon, a mathematician and the developer of psychohistory uses algorithmic science to predict the collapse of the Galactic Empire.

DataOps is even better than psychohistory, with sufficient data we can prevent the collapse of the Galactic Empire. Or. Well… Maybe we can use DataOps to prevent the Empire from forming in the first place.

In the meantime, DataOps can help us to mitigate and solve a lot of problems and generate insights we would never see with manual data exploration. That DataOps helps centralize data management to provide a single source of truth dispenses with huge amounts of time that everyone would otherwise spend searching for and validating a key piece of data.

  1. Real-Time Data Analytics – Using last year’s or quarter’s statistics to make decisions today about the future is a step up from guessing. What’s the impact if one of your lead developers changes jobs? You’ll see the impact on your team performance and sprint planning in the immediate days that follow and adjust future Sprint planning accordingly.

  2. Identify Valuable Data – With billions and billions of connected devices, we’re able to collect enormous amounts of data on everything from code churn to the photosynthetic radiation levels in soil or when someone orders a Pizza on their smartphone. But, what’s the relationship between these and millions of other data points? That’s where the value of data is found.

  3. Improved Data Quality – Like “fake news” – fake data or wrong data doesn’t help anyone, suffice that DataOps applies great effort to provide business users with accurate and complete data, augmenting it with “easy-to-understand” visual aids.

  4. Self-Service – DataOps also aims to make it easier for business users to create their own reports combining different sets of data without the need for creating dashboards for every case. Previously, it could take weeks for development teams to respond to user requests for different types of reports.

  5. Improve Team Collaboration and Focus -DataOps depends upon business users providing a continuous feedback loop to software developers and data specialists of all stripes to continuously improve data value. This increased interaction helps keep different teams focus and aligned on business objectives – instead of competing team metrics.

With the right data, DataOps can provide the insights needed to help solve any problem or find value in the relationship between different data points.

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