Guide: DataOps

DataOps vs DevOps

If you are running or working in a software development company, you may be familiar with DevOps, which is short for ‚Äėdevelopment operations‚Äô. It brings together the development and operations teams in an organization, allowing for faster and more efficient development. However, there are several other methodologies that integrate several departments with operations, including DataOps.

DataOps is short for ‚Äėdata operations‚Äô, and it is an advanced and emerging approach to data management and operations. It is used to combine technologies with business processes that allow for automated data management, collection, transmission, and organization.

One of the most common things people are confused by is the difference between DataOps and DevOps, which may seem similar but are quite distinct in essence. 


In this guide, we talk about:

DataOps vs DevOps - The Comparison

Generally, DevOps is used for product development, whereas DataOps accelerates data processing and helps in aligning data with business objectives. Let’s look at the definition of both of them one by one.

What is DevOps?

Development Operations, or DevOps, fuses together the technical aspects of product development with the operational aspects of product delivery. This methodology was developed as a solution for organizations to keep up with the rapid pace at which mega-companies like Google, Apple, and Facebook develop and release new products and solutions.

DevOps can be broken down into two main components. 

  • The development component addresses the planning, designing, and creation of software packages to be delivered or released.¬†
  • The operations component is responsible for monitoring the delivery and release of the product in question.

This is a continuous process that features a lot of back-and-forth correspondence between the development and operations teams. Even when new products are released, the operations department gives feedback to the development team, which helps them plan updates, upgrades, or even tweaks and fixes for any problems in the product.

Why Do You Need DevOps?

DevOps not only brings down the product development cost, but also accelerates release cycles. It also does away with separate teams for engineering, IT operations, development, quality assurance, and other departments, and also shortens the product lifecycle that the product has to go through before being sent for delivery.

Apart from this, DevOps is also a suitable method for improving the security and flexibility during the production and delivery process, and also removes any external influences or hurdles that may slow them down. 

Ever since DevOps has been invented and introduced, organizations are able to deliver new products and services innovatively. Since they can deliver software much faster, it gives them time to start working on new products and updates much quicker.

What is DataOps?

Data Operations, or DataOps, is quite similar to DevOps, especially the agile and continuous delivery aspects. Other than that, this methodology is much different from DevOps, and it also features different goals and milestones. 

Basically, DataOps integrates data analytics and operations teams, helping them deliver analytic solutions and products at a much faster rate and with complete accuracy and reliability.

In today’s world, data is more valuable than any other commodity, which is why businesses are ready to do anything to gather insightful and actionable data they can use to deliver data-driven solutions and products. 

DataOps came into being as a method for data teams to tackle the growing need for relevant and verified data. It was based on the DevOps methodology to create and deliver faster results.

Why Do You Need DataOps?

DataOps is designed to encompass several manufacturing methodologies, including lean manufacturing, process control, and agile development. In simpler words, it helps organizations find suitable data for a particular application. DataOps teams include the input of data scientists, data analysts, IT operations specialists, application developers, and business managers to provide business insights.

Moreover, DataOps also optimizes and adjusts existing data models, visualizations, reports, and dashboards for organizations to achieve their business goals. Like DevOps, it reduces the role of several departments and teams in the production and delivery process, which cuts down on the time and money required to come up with an analytics solution.

Automation is the cornerstone of DataOps, and it helps data management and operations teams to work together and build rapid data pipelines, so that businesses can derive more value from their data.

Similarities Between DataOps and DevOps

If you put the two methodologies together, you would notice that both of them employ the use of the agile methodology. Agile development has a consistent and iterative approach that ensures rapid delivery in smaller sub-sections. Rather than several departments working separately and working to develop a singular package, the agile methodology involves teams collaborating to develop small modules of the application much faster.

Agile management also helps cut down on the time taken by data teams to identify bugs and troubleshoot errors in the software. It also fosters constant communication between different teams, as well as the frequent exchange of feedback. This way, development teams can act on the data and insights provided by data teams to alter their strategies in real-time, and also fix any bugs, issues, or mistakes in the product.

DataOps is More Than DevOps for Data

Ever since DataOps has become a common practice among organizations, many people argue that it is simply ‚ÄėDevOps for data‚Äô, but things are much different in reality.¬†

The fundamental outputs of these two methodologies are much different. 

  • DevOps deals with the development and delivery of software products.
  • DataOps involves the development, testing, and release of data products and solutions.

Data is much different from software, which is why the composition of both teams is also entirely different. Generally, DevOps requires skills like software engineering, programming, development, application integrations, quality control, security, and others. Therefore, it encompasses professionals from engineering, IT operations, development, QA, user experience, and design.

On the other hand, DataOps has a much more diverse skill set, including data science, data management, analysis, integration, statistics, IT operations, application engineering, data engineering, and governance. As a result, a DataOps team would include professionals from business units, data science, governance, management, operations, IT operations, and compliance as well.

Even though there are several differences between the two, the delivery cycles are similar for DataOps and DevOps, and they involve three basic steps: build, testing, and release. In DevOps, the software is developed, tested, and released. On the other hand, the DataOps process involves several extra steps in between these three stages, thus allowing teams to ensure data validity and application.

DataOps vs DataSecOps

As if DataOps and DevOps weren’t enough, there is another methodology that is used by organizations to optimize and enhance collaboration between several teams: DataSecOps.

DataSecOps is similar to DataOps, but it also adds the involvement of security protocols, principles, and processes in the landscape. It is an agile and holistic methodology that aligns data solutions with the rapidly changing data, and also facilitates the privacy, safety, and governance of data. Data-related processes have changed drastically over the years, and DataSecOps helps organizations keep up with these changes.

The addition of security as a first class citizen into the DataSecOps process ensures that all of the data projects and operations are kept secure. This also means that security is embedded into every step of the process, rather than being added to the final stage through a security check in a data project or audit. Therefore, the emphasis on security in this methodology is carried throughout the process from design to delivery.

If you compare DataOps with DataSecOps, it is safe to say that DataSecOps is an extension of the former, and it makes security a continuous part of the data operations. Since data security and integrity are vital to development processes in any organization, integrating the ‚ÄėSec‚Äô part into DataOps allows you to prevent any security issues that occur during the development and delivery process.


Let’s understand the difference between DataOps and DataSecOps with a more comprehensive example.

Suppose you are working on a data project with the DataOps methodology. This would mean that security would come towards the end of the project, and it would lead to a lot of security issues and risks building up along the process. As a result, you will need to revisit earlier stages of the project and this would cause an increase in cost and project completion time.

On the other hand, if you employ a DataSecOps approach, security will be a continuous part of the development project. Therefore, any security issue that pops up will be immediately dealt with by the security experts that are in the team. This way, you will keep mitigating most of the problems of your project in real-time. By the time you reach the final phase, you will have a refined solution with fewer bugs or mistakes.

DataSecOps With Satori

Satori, The DataSecOps platform enables organizations to streamline their access to sensitive data by applying codeless fine-grained access control, and by enabling data owners to apply simple access policies to their data, including self-service data access. 


DataOps, DevOps, and DataSecOps are all similar in the sense that they aim to solve the need to scale delivery. The key difference is that:

  • DataOps focuses on the flow of data and the use of data in analytics.
  • DevOps focuses on the software development and delivery lifecycle.¬†
  • DataSecOps takes DataOps a step further by incorporating security as a continuous part of the data operations processes.

The table below gives a brief comparison of all three processes.

Data Operations
Development Operations
Data Security Operations
Employs agile approach
Involves data management and IT operations teams
Involves product development and IT operations teams
Involves data management, IT operations, and security teams
Uses Build, Test, Release with several steps in between
Uses Build, Test, Release
Uses Build, Test, Release with security in every stage
Faster delivery of data analytics solutions
Faster development and release of software products
Faster delivery of data products
Ensures structured and accurate data
Ensures faster deployment with the least amount of bugs and issues
Ensures data security, safety, and governance
Continuous integration and delivery
Offers companies automated data pipeline
Offers companies the chance to release their product faster
Offers maximum security for every step of the data project

Last updated on

November 15, 2021

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