DevOps is still an extremely popular methodology that requires no introduction in the modern technological landscape. In light of the DevOps methodology’s achievements, new software development approaches have begun incorporating some of its characteristics into specialized subfields.
One model developed to assist data management teams in harnessing the power of automated data orchestration and developing intelligent, data-driven systems are DataOps. The DataOps discipline streamlines the process of designing, developing, and maintaining data-centric applications by bringing agile software development principles into data analytics.
This article explores the fundamental differences between the methodologies of DevOps and DataOps, the similarities between the two, and the use-case benefits each offer.
What is DevOps
DevOps implementation comprises a collection of practices and tools that facilitate collaboration between the teams responsible for software development and information technology operations (SDLC) throughout the software development lifecycle.
Continuous Integration, Continuous Delivery, and continuous deployment pipelines, which are implemented by automation that DevOps enforce, enable a quicker time-to-market without negatively affecting the quality of the code.
Through the entirety of the software’s lifecycle, the methodology encourages randomly sequenced iteration, which makes rapid application development and delivery possible.
Why Businesses Use DevOps
- Tools and procedures for DevOps make it possible to innovate more quickly, bring products to market more quickly, and increase profits. Typically, when an organization implements DevOps, they reap several benefits.
- A marked improvement in both communication and cooperation. The application development, release management, and operations teams benefit from having their operational silos broken down by DevOps because it facilitates easier collaboration and the sharing of resources.
- Cost savings. By shortening delivery cycles, DevOps helps reduce maintenance and upgrade costs. Time spent recovering from disasters is cut down. Small, managed batches are used to implement the build, test, and deploy cycles by DevOps.
The Typical Workflow of a DevOps Team
DevOps services are a recursive process that continually cycle between the following five stages:
- A blueprint for the future of the company
- Development and Testing
- Deploy (Release of the application)
- Operate (Ongoing maintenance)
In addition, the feedback on usability is looped back into the workflow during the operating phase, which assists in refining the business vision for iterative and responsive application development.
What Exactly does “DataOps” Stand For
Modern businesses’ various applications and management systems produce data in ever-increasing quantities. The DataOps methodology helps organizations to achieve their goals by standardizing the technological and cultural shifts that are necessary to:
- Decrease the costs associated with managing the data
- Improve data quality
- Reduce the amount of time needed to bring data-centric applications to market
While doing so, DataOps closes the gap between the collection of data, its analysis, and making data-driven decisions. This enables businesses to deliver analytical insights more effectively, improving their business value.
The Advantages of Using DataOps
The DataOps initiative aims to improve the quality of data analytics while simultaneously reducing the time required for each stage of the data lifecycle. The following is a list of some of the benefits that the DataOps methodology offers:
• Computerizing formerly laborious methods of data collection and analysis
• Continuous monitoring of the data pipeline
• The segregation of production records
• The consolidation of data definitions and their collaborative use
• Improving the data stack’s adaptability and reusability
• Enabling controlled data access
DataOps in Modern App Development
To gain deeper analytical insights and provide a better overall experience for their customers, businesses are integrating artificial intelligence and machine learning models into their digital products and services.
The following are some of the most important aspects of contemporary application development that can benefit from data operations:
• Interaction with a self-service terminal
• Services for data curation and governance of the data
• Keeping an eye on logs and events
• Vulnerability scanning
• Searching and indexing the results
• Analyses of the market
DevOps vs. DataOps
The primary objective of both DataOps and DevOps is to improve the speed and efficiency with which a product is developed and to automate as much of the process as possible. The two approaches share several similarities, including the following:
• Both methodologies use agile delivery methods to cut down on delivery times.
• Both require cross-functional cooperation between several different teams
• Both extensively use various automated tools to advance development more quickly.
The following are the primary distinctions between the two research approaches:
- A Criterion for Quality
The primary objective of DevOps is to accelerate software development cycles while maintaining a high level of product quality. DataOps emphasizes collecting high-quality data to facilitate faster and more reliable BI insights.
- Automation of the Delivery Process
The primary focus of DevOps is the automation of server configurations and version management.
DataOps is primarily concerned with automating the processes of data acquisition, modeling, Integration, and curation to deliver high-quality data.
Summarizing DevOps & DataOps
The current climate of technology is marked by a high degree of flux. Businesses rely on highly scalable, efficient, and secure applications to achieve a competitive advantage in their respective industries.
Businesses need to adopt the appropriate model that will assist in developing applications that are not only agile but also efficient and secure.
The DevOps methodology remains the most well-liked option in the list of different software development approaches. On the other hand, DataOps is centered on the delivery of data-driven applications.
Companies looking to improve their development lifecycles can benefit from advanced innovation and competitive advantage by implementing either DataOps or DevOps. The phases of software development, known as build, test, and deploy, are carried out in a manner that is distinct between the two methodologies.
I am Pratik Mistry, a rare mix of technologist and vice president in sales at Radixweb. My passion lies in helping companies to grow revenues by delivering top notch custom software development solutions and building value-based partnerships. When not driving high-impact go to market strategies, I love to try new cuisines and going to the movies.