What is Dataops (Data Operations)? Difference between DataOps and DevOps

What is Dataops (Data Operations)?

During an analytics project, companies spend 80% of their time on tasks like data preparation rather than data analysis. Businesses, therefore, focus on gaining the agility to improve the data processing speed and increase the data quality to derive key insights. This focus requires an agile data management approach like DataOps.

DataOps is a process-oriented data management practice focused on improving communication, integration and automation of the data flowing between data managers and consumers within an organization. DataOps combines DevOps, agile management, personnel, and data management technology, providing a flexible data framework that delivers the right data to stakeholders at the right time.

DataOps uses technology to automate the design, delivery, and management of data delivery with the right level of governance and metadata to improve data value in today’s dynamic environment. It creates predictable delivery and changes the management of data, data models, etc., to deliver value faster.

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Why do you need DataOps?

                  Source: https://www.devopsschool.com/blog/why-do-we-need-dataops/ 
  • DataOps promotes agile development, without which data projects may take years, rendering any collected insights useless. Multiple levels of management cause delays and create bad data. DataOps ensures that the code gets into production quickly, delivering value continuously. Agile methodology promotes short, sharp sprints, resulting in faster business insights.
  • In this complex data landscape, understanding the data can be tough. DataOps unlocks value from the data by integrating testing into the data analytics pipeline and providing quality control. It enables clear measurement and transparency of results to help make competitive business decisions.
  • Numerous building blocks are involved in the data lifecycle, and automation can cut down on manual, time-consuming tasks like data reporting and quality checks. DataOps is the science of automating the data analytics lifecycle to minimize errors, improve data quality and promote agility.
  • A Properly designed DataOps process streamlines the data process and creates harmony between the different pockets of innovation. It makes the process adaptable and easy to maintain.
  • DataOps relies heavily on communication and teams talking with one another. It bridges the gap between the ones who collect the data, those who analyze it, and those who use the insights to good use.

How does DataOps work, and what are its components?

DataOps uses numerous technologies, including Artificial intelligence (AI), Machine learning (ML) combined with agile methodologies, and various data management tools that optimize data processing, testing, provisioning, deployment, and monitoring. 

The working of DataOps is based on the principles of the following aspects.

Agile Methodology

At the heart of DataOps is a focus on collaboration and innovation. Agile methods in DataOps create an environment that reduces friction between IT and business groups. This method is more useful when requirements change rapidly. It can also significantly reduce the time it takes to find the data and deploy the data model into production, allowing IT teams to quickly change and adapt to the pace of the business group. Business teams are also now aware of the data science team’s work, allowing greater transparency.

Lean Manufacturing

Applying lean manufacturing practices minimizes waste, increases efficiency without sacrificing product quality, and significantly saves time. DataOps uses Statistical Process Control (SPC) to monitor and control data analytics pipelines. With SPC, it continuously monitors the data passing through the operating system and verifies its functionality. Automatic alerts can notify the team of data analysts when anomalies occur.

DevOps

DevOps focuses on continuous software delivery by using on-demand IT resources and automating the integration of code, testing, and deployment. This integration of software development and IT operations reduces time-to-market while minimizing errors and troubleshooting. Following DevOps principles, data teams can collaborate more effectively, perform analysis quickly, and deploy models faster. 

DataOps in action

Streamlined DataOps processes include toolchain and workflow automation, where data enters the system from the source, changes over time, and flows downstream for transformations, models, visualizations, and reports fed into the system. It can be viewed as a production environment that directly leverages existing workflows, tests, and logic to derive value and keep data quality under control. This ensures that the code or toolset stays constant, and the data continues to change and update downstream, keeping all the insights live and active. 

Another concurrent activity is generating new code, tests, models, and functions for existing code/tools that manipulate data. This speeds up analysis and enhances pipeline feedback mechanisms which can be handled well using fixed datasets and containerized environments with parameter and version control so that developers, testers, and other stakeholders can accelerate changes to production.

Difference between DataOps and DevOps

DataOpsDevOps
Its goal is to improve the quality of products by better aligning data and the data teams towards business goals.It focuses on encouraging collaboration across teams to shorten the application development cycle and improve its quality.
The challenges involved is that the goals may differ between the data teams and business employees.In DevOps, resistance to change within the organization can hold back the adoption. Moreover, different toolkits may be required by development and operations teams.
The target teams include a data analytics team of data engineers, data scientists, developers and line-of-business employees.Here, the target team involves software development members and IT operations.

References:

  • https://www.gartner.com/en/information-technology/glossary/dataops
  • https://rivery.io/what-is-dataops/
  • https://research.aimultiple.com/dataops/
  • https://www.wipro.com/analytics/the-importance-of-dataops-why-it-matters-and-how-to-get-started/#:~:text=DataOps%20helps%20overcome%20the%20hurdles,Lean%20Manufacturing%2C%20Agile%20and%20DevOps.
  • https://streamsets.com/why-dataops/what-is-dataops/
  • https://www.devopsschool.com/blog/why-do-we-need-dataops/
  • https://hevodata.com/learn/dataops/
  • https://www.xenonstack.com/insights/data-operations
  • https://meltano.com/blog/what-is-dataops/
  • https://www.devopsschool.com/blog/how-dataops-works-and-architecture/#:~:text=DataOps%20uses%20statistical%20process%20control,notified%20through%20an%20automated%20alert.
  • https://hevodata.com/learn/dataops/
  • https://www.ibm.com/downloads/cas/GLNAZBXN
  • https://www.xenonstack.com/insights/data-operations
  • https://www.ibm.com/downloads/cas/GLNAZBXN
  • https://www.highbyte.com/blog/four-practical-use-cases-for-industrial-dataops
  • https://www.xenonstack.com/insights/data-operations
  • https://rivery.io/what-is-dataops/
  • https://www.techtarget.com/searchbusinessanalytics/feature/Key-differences-in-uses-of-DataOps-vs-DevOps

I am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.