Is DataOps "DevOps For Data"?

In this post, we’ll discuss how DataOps differentiates from DevOps and explore how they differ from DataSecOps, another critical aspect of data management.

The DevOps for applications deployment has been a major catalyst for the shift in how IT is done, and it’s helped to make companies more agile and responsive to their customers. DevOps principles have been widely adopted in the software development world, but are extensively compared to DataOps. While this is a common misconception, DevOps and DataOps are two sharply different pursuits.

Here, we’ll take a look at DataOps vs. DevOps, as well as the main differences between them. We’ll also explore how they differ from DataSecOps, another critical movement for those managing data throughout its lifecycle.

What Is DataOps?

DataOps is a relatively modern term, first coined in 2014 by Lenny Liebmann. At its core, DataOps is a process-oriented approach for improving the quality and lowering the cycle time of data analytics used by analytic and data teams. DataOps unifies data analytics and operations teams, providing analytical solutions and products more quickly and accurately.

Data has never been a more valuable commodity than it is in today’s environment, which is why businesses are willing to go to extreme measures to get data that can be used to provide data-driven solutions and goods. While DataOps began as a set of best practices, it has evolved into a unique methodology for data processing, preparation, and analytics. 

Why Is DataOps Important for Data-Driven Organizations?

DataOps is crucial for data-driven organizations because it helps to improve the quality and speed of data analytics, or the “time-to-value” for data projects. DataOps helps optimize the entire data pipeline, from data acquisition to analysis. The quality and speed improvements can result in several benefits which include:

  • More reliable and timely insights

  • Increased efficiency and productivity

  • Reduced costs

Furthermore, DataOps can help to improve the overall data quality. This is because it brings together all stakeholders in the data pipeline, including data scientists, engineers, and others. By working collaboratively, they can identify and resolve issues early on before they cause problems downstream. DataOps also aims to continuously improve and change data models, visualizations, reports, and dashboards to meet company objectives.

As an organization grows and enjoys success, it needs more data to make informed decisions. As a result, data ingestion is accelerating at an increasingly rapid pace. The DataOps formation requires high-quality data and analytics solutions with greater dependability as time goes on.

DataOps vs. DevOps

While both are based on agile structures designed to expedite working cycles, DevOps focuses on product development. In contrast, DataOps is focused on data management and speed to delivery. In addition, DevOps is centered around the application layer, while DataOps encompasses the entire data supply chain, from acquisition to consumption.

Furthermore, DevOps emphasizes collaboration between development and operations teams, which can often be siloed in an organization. In contrast, DataOps highlights the relationship between all data consumers, which can mean many more data stakeholders. This is critical to secure data integrity throughout all aspects of product development.

DataOps is not DevOps for data, but DevOps laid the foundation for DataOps. DevOps methodologies help implement and support data operations with agility, continuous delivery, focus on automation, etc. Because DevOps focuses on making IT more agile by breaking down functional silos, it also helps with DataOps in that area.

Similarities Between DataOps and DevOps

Many of the principles that power DataOps were derived from similar principles in DevOps. Companies require DevOps to produce a high-quality, uniform software and feature development framework. When it comes to being data-driven, the companies rely on these characteristics to make agile data engineering and analysis.

As DataOps uses the same DevOps toolchain, leveraging it is pretty simple for organizations with a DevOps framework in place. The following are some of the main ideas taken from DevOps by DataOps:

  • Rapid growth

  • Reuse and automation

  • Concentrate on delivering market value

  • Automatic testing and code promotion

  • Continuous integration and continuous delivery (CI/CD)

Differences Between DevOps and DataOps 

Despite the parallels between DevOps and DataOps’s foundations, there are several significant distinctions.

  • The Method: The activities of DataOps and DevOps have comparable interactive characteristics. However, the latter has a data pipeline and an analytics development process that are both live and interacting; the former comprises a software development and delivery method.

  • Orchestration: Application source code does not need extensive orchestration in the DevOps methodology. In DataOps, the data pipeline and analytics development orchestration are required components. There is typically no such coordination of pipelines in application development and DevOps procedures, although orchestrating dataflows happens all the time (for example, ETL/ELT processes).

  • Data Administration: In DevOps, data schemas, administration, and authorization changes are marginal. In DataOps, they are the front stage.

  • The Human Element: The personalities and skillsets of the DataOps and DevOps users are diverse. DevOps deals mainly with engineers, while DataOps deals with many personas, some of them are less technical.

  • Instruments: The birth of DataOps was ushered in by DevOps, and the tools required to support it are still in their early stages. While testing in DevOps is primarily automated, DataOps does not have an identical extravagance - most users must modify testing automation software or develop their own from scratch.

DataSecOps, And Why It’s Important

If you thought that DataOps and DevOps were just about developing and managing data, you’re in for a surprise. DataSecOps is the marriage of DataOps and information security, or cybersecurity. As you can imagine, this union is vital for organizations looking to move towards a more data-driven future.

In the same way that DataOps and DataSecOps are comparable in that they focus on data analysis to improve security posture, they are different in their emphasis on security standards, ideas, and procedures. Over time, data-related practices have evolved dramatically, and DataSecOps aids businesses stay ahead of the curve. It’s a dynamic, all-encompassing mindset for integrating data solutions with rapidly shifting data and enabling data governance, privacy, and security.

The inclusion of security as a primary priority within the DataSecOps method ensures that all data projects and operations are kept safe. This also implies that security is included at every stage of the process rather than being put in at the end through a data project or audit. This approach aims to ensure that fast data projects don’t come with additional security risks. As a result, security is emphasized throughout the process, from design to delivery.

If you look at DataOps vs. DataSecOps, it’s clear that the latter is a subset of the former, with security being integrated into data operations continuously. Because data security and integrity are critical to every organization’s development processes, incorporating the ‘Sec’ component into DataOps helps you avoid any data-security problems throughout the data lifecycle.


DevOps and DataOps are critical methodologies for data-driven organizations, but they differ significantly. DataOps is not DevOps for data, but it is an agile approach that is especially important for data-centric organizations with many data consumers, data producers, and other data stakeholders.

Relevant Blogs:

Why Is Branching in GitOps a Bad Idea? 

MLOps for Enterprise AI 

The Rise of the Data Reliability Engineer 

Getting Started With Apache Cassandra

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