What do you need to know about DataOps?

What is DataOps? Things around it that you need to know

The challenges of collecting data grow with the volume of digital information. To make strategic decisions, business teams need real-time data. They need strategies and powerful software that can shorten the cycle time of data analysis and automate processes. The DataOps technique characterizes procedures and methods for handling data so it can help solve many problems. 

Let’s find out a bit about this strategy and how data engineering services can help you implement it.

DataOps definition

For starters, let’s see what DataOps actually means.

DataOps definition by Gartner:

“DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization.”

DataOps definition by Forrester:

“DataOps is the ability to enable solutions, develop data products, and activate data for business value across all technology tiers, from infrastructure to experience.”

DataOps (data operations) is a methodology for the conduct of analytical teams and activities performed on data in preparation for analysis or dissemination or presentation (reports, visualizations). They include all procedures related to data acquisition, processing, storage, management, and protection. What is more, they constitute a package of activities in the area of data management that enable their most effective use.

The benefits of applying the DataOps strategy


Because the DataOps strategy is based on an Agile approach, the cycle time of the data analysis application is shorter. Besides, everyone can access the data, so they can get real-time insight.


DataOps focuses on eliminating errors and improving data quality, usability, completeness, and transparency.


DataOps is a unified data management strategy that copes well with recurring processes.


Clear and transparent results, as well as secure and close monitoring of how data is distributed, make better data governance.

How does DataOpa work?

DataOps bases its work on the principles of three approaches, i.e. Agile Methodology, Lean Manufacturing, and DevOps. 


It is a project management system that emphasizes self-organization, adaptive planning, flexible development, continuous changes, and fast delivery of results. Its main assumption is to shorten many task deadlines without compromising on quality. By using the principles of the Agile methodology:

  • Friction between IT teams and business teams is reduced
  • Faster data acquisition
  • IT teams make decisions quickly and react to business changes, so business operations are not delayed.


It focuses on reducing waste and delivering value to the customer at every stage of the process. Data teams build pipelines (ETL/ELT) to transform data into insightful reports or visualizations. In addition, they bring models to production as well as possible solutions to pipeline problems. Using the Lean manufacturing approach:

  • Saves time
  • Improves efficiency
  • Minimizes waste
  • Delivers high-quality products.


DevOps is defined as a software strategy that uses automation to accelerate the build lifecycle. The main goal of DevOps is to save time when making changes related to the software and verify that the implementations were made correctly. DevOps is about cooperation and constant communication between software development and IT maintenance. Both exchange their knowledge, complement each other and communicate. By following DevOps principles, data teams can collaborate better and deploy faster.

5 types of DataOps tools

There are many DataOps tools on the market. We can divide them into 5 categories.


These platforms practically cover most of the components necessary for data management (acquisition, transformation, analysis, and visualization). So if you want to manage data on one platform, this solution is for you.


These tools focus on DataOps processes. They:

  • Provide centralized management of complex data pipelines,
  • Increase quality and reduce cycle time,
  • Offer continuous testing and monitoring of components.

They are a great solution for companies that have already invested in data management tools and don’t want to introduce another tool.


Component tools can perform a maximum of several tasks over the data lifecycle. So if you choose this solution, you will have to purchase a lot of tools. For example, you will need a different product for data storage, yet different for data sharing.


These tools focus on a particular domain of DataOps. For example, you can use CloudOps to migrate your data to the cloud. In turn, to automate updates to the data warehouse, you can use the DW Automation Tool.


The market is full of open-source DataOps tools. They can include, among others:

  • Apache Airflow, which is one of the most common tools for orchestrating data,
  • GitHub, which is a source code history management tool.


It becomes increasingly difficult to manage data effectively as the number of sources grows. To ensure scalability and repeatability, you need a flexible and strong data management strategy. DataOps is a strategy that enables

  • Fast innovation and experimentation
  • Achieving high quality and low error rates
  • Collaboration between people, technology, and environments
  • Monitoring and transparency of results

James Morkel

Tech website author with a passion for all things technology. Expert in various tech domains, including software, gadgets, artificial intelligence, and emerging technologies. Dedicated to simplifying complex topics and providing informative and engaging content to readers. Stay updated with the latest tech trends and industry news through their insightful articles.

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