What is Dataops (data operations)?
During an analytics project, companies spend 80% of their time on tasks such as data preparation rather than data analysis. Companies are therefore focusing on agility to improve data processing speed and increase data quality to gain key insights. This orientation requires an agile approach to data management as Data operations.
DataOps is a process-oriented data management practice focused on improving the communication, integration, and automation of data flowing between data managers and consumers within an organization. DataOps combines DevOps, agile management, people, 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 enhance the value of data in today’s dynamic environment. It creates predictable delivery and changes the handling of data, data models, etc., to deliver value faster.
Why do you need DataOps?
- DataOps promotes agile development, without which data projects can take years, rendering the information collected useless. Multiple levels of management cause delays and create erroneous data. DataOps ensures that code goes into production quickly, driving continuous value. The Agile methodology promotes short and precise sprints, which results in faster business insights.
- In this complex data landscape, understanding data can be challenging. DataOps unlocks the value of data by integrating testing into the data analytics pipeline and ensuring quality control. It allows for clear measurement and transparency of results to help make competitive trading decisions.
- Many building blocks are involved in the data lifecycle, and automation can reduce time-consuming manual tasks such as 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 different pockets of innovation. This makes the process adaptable and easy to maintain.
- DataOps relies heavily on communication and teams communicate with each other. It bridges the gap between those who collect the data, those who analyze it, and those who use the information wisely.
How does DataOps work and what are its components?
DataOps uses many technologies, including artificial intelligence (AI), machine learning (ML) combined with agile methodologies and various data management tools that optimize processing, testing, provisioning, deployment and data monitoring.
The operation of DataOps is based on the principles of the following aspects.
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 most useful when requirements change quickly. It can also dramatically reduce the time it takes to find the data and deploy the data model to production, allowing IT teams to change quickly and adapt to the pace of the business group. The business teams are also now made aware of the work of the data science team, allowing for greater transparency.
Applying lean manufacturing practices minimizes waste, increases efficiency without sacrificing product quality, and saves a lot of 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 data analyst team when anomalies occur.
DevOps focuses on the continuous delivery of software using on-demand computing resources and automating code integration, testing, and deployment. This integration of software development and IT operations reduces time to market while minimizing errors and troubleshooting. By following DevOps principles, data teams can collaborate more effectively, perform analytics quickly, and deploy models faster.
DataOps in action
Streamlined DataOps processes include toolchain and workflow automation, where data enters the system from source, changes over time, and flows downstream for transformations, models, visualizations and reports fed into the system. It can be thought of as a production environment that directly leverages existing workflows, tests, and logic to extract value and keep data quality under control. This ensures that the code or toolset remains constant and the data continues to change and update downstream, keeping all information live and active.
Another concurrent activity is to generate new code, tests, models and functions for existing code/tools that manipulate data. This speeds up analysis and improves pipeline feedback mechanisms that can be well managed using fixed datasets and containerized environments with parameter and version control so that developers, testers and others stakeholders can speed up production changes.
Difference Between DataOps and DevOps
|Its goal is to improve product quality by better aligning data and data teams with business goals.||It aims to encourage collaboration between teams to shorten the application development cycle and improve its quality.|
|The challenges involved are that the goals may differ between data teams and corporate employees.||In DevOps, resistance to change within the organization can hinder adoption. Additionally, different toolkits may be required by development and operations teams.|
|Target teams include a data analytics team comprised of data engineers, data scientists, developers, and line-of-business employees.||Here, the target team includes members of software development and IT operations.|
- https://www.wipro.com/analytics/the-importance-of-dataops-why-it-matters-and-how-to-get-started/#:~:text=DataOps%20helps%20overcome%20the% 20 obstacles, Lean%20Manufacturing%2C%20Agile%20and%20DevOps.
I am a civil engineering graduate (2022) from Jamia Millia Islamia, New Delhi and have a keen interest in data science especially neural networks and their application in various fields.
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