Ever since its introduction, DevOps, the software development approach to agile and quality software development and delivery, has initiated the introduction of various new software engineering methodologies and disciplines that facilitate collaboration between dev, operations, and other engineering teams, allowing them to build and deliver new software or features consistently and reliably.

DataOps is one such emerging discipline, introduced as a data analytics best practice that has now evolved into a new and independent data analytics approach. The methodology brings together DevOps teams with data engineers and data scientists to facilitate support to data-focused enterprises in their technical journey by borrowing DevOps practices and principles that improve the velocity, quality, predictability, and scale of the data analytics process.

But what is the significance of this methodology? What role does it play in DevOps?

Well, to answer these questions and more, we are here with our detailed discussion of DataOps.

What is DataOps?

Nowadays, when organizations across industries are becoming more and more data-driven, using an excessive amount of data to deliver excellence to customers consistently, maintaining a thorough understanding of data assets can be challenging, which is further made difficult by the constantly changing, complex data environments. It is to help data-driven organizations overcome such challenges, DataOps was introduced by Lenny Liebmann in 2014 in a blog post on the IBM Big Data & Analytics Hub.

Later defined by Gartner as a "collaborative data management practice", DataOps or Data Operations is a combination of tools and methodologies that streamlines the development of new analytics and ensures a high level of data quality.

It orchestrates, monitors, and manages the data factory and helps improve communication, integration, and data flow automation between data managers and consumers across an organization, facilitating faster delivery of value. This automated, cross-functional, process-oriented methodology accelerates lifecycle and improves collaboration, orchestration, quality, security, access, and ease of use.

Moreover, as suggested by its name, the methodology borrows heavily from DevOps. However, it is important to understand that DataOps is a result of collective principles of three major technologies and practices: DevOps, Agile, & Statistical Process Controls that enable it to bring speed and agility to the end-to-end data pipeline process, from collection to delivery, and offer necessary support to teams with automation technology, improving their productivity and delivering huge efficiency gains in project outputs and time.

Characteristics of DataOps:

Since its introduction, DataOps has effectively improved data efficiency and helped organizations convert raw sources into valuable intelligence. Moreover, it has become a key to reducing data costs, accelerating analytics, and enabling better outcomes.

Characteristics that define DataOps are listed below:

  • Offers customizable metadata fields, tags, and labels for improved data discovery and recommendation success.
  • Supports Cloud agnostic integrations to unify data sources, connecting every cloud or on-premises source, etc.
  • Promotes extensibility across existing infrastructure that helps highly data-driven enterprises to achieve streamlined, accelerated, optimized data ecosystems and transparency across the entire supply chain.
  • Has self-services data marketplace for easy discovery, selection, and provisioning of data, reports, repositories, etc., acceleration of analytic outcomes, and reducing data costs.
  • Promotes analytic collaboration by breaking down the barriers of remote work, departmental divisions, and competing business goals, automated, customizable data quality, and secure and protected data and compliance.
  • Orchestrates pipeline, based on the DevOps principle of Continuous Deployment, to enhance and extend analytics.

Steps to Ensure Successful DataOps Implementation:

Though there is no one approach to implementing DataOps, there are some key areas of focus that can help an organization implement DataOps successfully and enjoy the various benefits it offers. These areas of focus include:

  • Democratize Data: Make sure the datasets are readily available to everyone, as it helps teams to learn and improve and build more cutting-edge applications.
  • Automated Test Implementation: Consistently run automated testing to ensure data quality and accuracy. Make sure the accuracy of inputs at the processing stage, then verify that it matches business assumptions/goals, and finally ensure the consistency of the results.
  • Version Control: One of the most critical aspects to implement in DataOps, Version Control helps team branch and merge as well as organize and manage the code changes and revisions. It is an effective way for keeping code in a known repository and facilitating disaster recovery.
  • Branch and Merge: Another important area of focus, branching and merging in DataOps enables data teams to maintain several coding changes in the analytics pipeline in parallel and helps boost data analytics productivity. It offers individuals the ability to run tests, make necessary changes, etc. in their environment without affecting the source code/pipeline.
  • Provide Isolated Environment: From preventing breaking schemas or mixing up models, with isolated environments teams can better ensure the accuracy of the data, whenever new data flows in.
  • Containerize and Reuse Code: By implementing containerization and reusing code, organizations can increase team productivity and cut out tedious tasks. Moreover, they allow teams to work with unfamiliar code, without affecting the essential programming and enhance their learning.
  • Build Parameters in Pipeline: Integrate your pipeline with the flexibility to respond to various run-time conditions by using parameters. Moreover, make your pipeline capable of accommodating different run-time circumstances and streamline your efforts.
  • Platforms and Open Source Tools: Leverage the various platforms and open-source tools available in the market for various processes like data movement, orchestration, integration, performance, development, deployment, continuous integration, and more.
  • Smash Silos: The points mentioned above will be futile and ineffectual if there is no collaboration between teams. Hence, collaboration is the most essential for successfully implementing DataOps.

Benefits of DataOps:

From bringing automation and cultural shift to an organization’s data project to encouraging collaboration and constant data innovation within a data-driven environment, DataOps offers a range of benefits to organizations, which have made it an extremely critical aspect of the data management and analytics process.

Therefore, here are a few benefits of DataOps that are playing a key role in its frequent adoption:

  • Offers real-time data insights and higher data quality.
  • Helps create a unified and interoperable data hub.
  • Enables better team communication and collaboration.
  • Increases transparency and reduces cycle time.
  • Offers a better understanding of data and what it represents.
  • Uses data automation to increase IT project velocity.
  • Increases the velocity, reliability, and quality of data analytics.
  • Reduces fragility through standardization and repeatability of data tasks.
  • Ensures compliance with industry regulations.
  • Prevents risks and secures enterprise data.
  • Promotes continuous software delivery.
  • Allows faster resolution of problems.
  • Results in higher employee engagement.

DataOps Best Practices:

From the steps defined earlier, we can conclude that the teams must adopt certain DataOps best practices to ensure process accuracy, speed, quality, and efficiency. These best practices are critical for DataOps implementation and help bring together a team with a variety of technical skills and backgrounds. Therefore, the DataOps best practices include:

  • Incorporate feedback loops for data validation.
  • At every stage of the data lifecycle, establish progress benchmarks and performance measurements.
  • Automate processes with the help of data science tools and business intelligence data platforms.
  • Define semantic rules for data and metadata early in the life cycle.
  • Automate and optimize processes to deal with bottlenecks and data silos.
  • Focus on continuous improvement of efficiency.
  • For experimentation, use disposable environments that mimic the real/production environment.

Difference between DataOps and DevOps:

DevOps and DataOps are two inter-related engineering concepts with different objectives. While DevOps has changed how software is developed, making it more agile, quality, and flexible. DataOps, a subset of DevOps, has changed how data products are being created, aiming to improve the quality and reduce the cycle time of Data and Analytics initiatives.

Other prominent differences between the two include:

Areas DataOps DevOps
Value Delivery

Data Engineering, Analytics, Data Science, & Business Intelligence.

Software Development and Delivery.

Quality Assurance

Involves Data Governance and Process Control.

Involves Code Reviews, Continuous Testing, & Continuous Monitoring.

Teams Involved

Data Analytics Team of Data Engineers, Data Scientists, Developers, and Line-of-Business Employees.

Software Development and IT Operations Teams.


Aligns Data and Data Team Towards Business Goals and Improves Product Quality.

Removes Silos, Encourages Team Collaborations, Shortens the Software Development Lifecycle (SDLC), and Improves Quality and Speed.


Data Teams and Line-of-Business have Different Goals.

Dev & Operations Teams Requires Different Toolkits. Resistance in Adopting DevOps within Organization.

However, DataOps and DevOps also share certain similarities. Both are agile approaches that remove silos, promote collaboration between teams, and increase agility. Moreover, DataOps uses DevOps processes to optimize code, product builds, and delivery to streamline and improve data quality.

Tools for DataOps:

Some of the most popular and beneficial tools that help organizations build and implement DataOps platform are:

  • Airflow: A scalable, dynamic, extensible, and elegant open-source workflow management tool, Airflow can help organizations with their DataOps journey, from viewing analytics as code to monitoring, reusing components, etc.
  • Data Build Tool (DBT): A command-line tool, DBT allows data analysts and engineers to transform their data by applying software engineering principles to analytics code and increasing data quality.
  • Reflow: A system for incremental data processing in the cloud, Reflow allows data analysts and engineers to compose existing tools using ordinary programming constructs and evaluating them in a cloud environment.
  • Meltano: An open-source, self-hosted, debuggable, and extensible tool, Meltano allows easy and free visualization of products, revenue, and marketing data in a single location. Moreover, it is an industry-standard platform for managing and orchestrating data pipelines.
  • Jenkins: Jenkins, the free and open-source automation server, is another prominent DataOps tool for an efficient and quality environment and deployment solutions and facilitating version control, modularization, and CI/CD.
  • Domino: A data science platform, Domino helps accelerate model development and delivery with infrastructure automation, seamless collaboration, and automated reproducibility. Moreover, it helps reduce waste and enforces better control during the process.
  • Open Data Group: A holistic approach, Open Data Group, now known as ModelOps, rapidly and iteratively moves models through the analytics life cycle and facilitates their successful deployment.


The increasing complexity of data ecosystems has resulted in new challenges for organizations in the past few years and prevented them from saving data costs, improving analytics, and achieving their business goals. It is to overcome these challenges and more, organizations are turning towards DataOps, the data management methodology, popular among software engineers as well as Artificial Intelligence and Machine Learning specialists.

This methodology, with its ability to remove siloed data, regulate multi-cloud data environments, compliance regulations, etc. has made it the one-stop solution for any data management, monitoring, and analysis issues.