DataOps and DevOps are two unique paths to take. Both are built on agile frameworks, which are meant to shorten work cycles. Whereas DevOps focuses on product development, DataOps attempts to shorten the time between data requirement and data success. At its best, DataOps reduces the time it takes for analytics to complete and aligns with business objectives.
When DataOps is implemented correctly, businesses can see significant changes in how they find, use, and extract value from their data.
What Is DevOps And What Does It Mean?
The concept of DevOps is not a new one. Development Operations, or DevOps, brings together the engineering and operational aspects of product development. Merging application development and IT operations has become industry standard, marking an essential milestone in a company’s maturity.
DevOps attempts to increase communication and collaboration between the two teams, but other companies have taken it a step further and implemented the DevOps model across their entire organization, focusing on breaking down barriers and encouraging cross-departmental cooperation. DevOps refers to the use of iterative software development, automation, and programmable infrastructure deployment and maintenance in a more limited sense.
Organizations that use a broad DevOps approach have a set of approaches in common. In DevOps, task automation is accomplished using continuous integration (CI) and continuous delivery (CD) systems. When development teams use CI, they notice faster and less disruptive code integration as well as better bug discovery. Jenkins, an open source automation server, and the GitLab platform are two examples of CI/CD tools.
DevOps brings real-time monitoring, incident management systems, configuration management, and collaboration platforms to enterprises. DevOps was born out of the need to keep up with the rapid development and introduction of new products by Google, Facebook, and other SaaS companies. DevOps is a continuous, looping process in software development, especially with today’s modern, agile approaches:
- In a continuous delivery and build lifecycle, development plans, creates, and packages software for delivery.
- The items are then released and monitored by operations.
- When new or additional development is required, Operations informs Development, which then plans for its implementation.
DevOps brings together several teams in order to cut product development costs and speed up delivery cycles.
Removing the barriers that exist between Engineering, IT Operations, Software Development, Quality Assurance, and other teams can help to enhance scalability, improve security and dependability, and enable faster and more efficient innovation. The DevOps philosophy has revolutionized how modern businesses deploy software, cutting months-long development cycles to mere minutes.
What Is DataOps And What Does It Mean?
Data Operations, or DataOps, is similar to DevOps in that both are built on agile, iterative thinking. While DataOps and DevOps have comparable methodologies, their goals are different. DataOps is designed to help companies produce high-quality data and analytics solutions at a faster and more reliable pace as time goes on.
As businesses battled to cope with an avalanche of data, data teams faced increasing pressure from the business to put that data to use. The DataOps technique was influenced by the DevOps methodology. DataOps was intended to take advantage of lean manufacturing, statistical process control, and, of course, agile development as underlying manufacturing approaches.
DataOps aims to discover the right data for the relevant application as rapidly as possible:
- To meet the business requirement for insights, it brings together business users, data scientists, data analysts, IT, and application developers.
- To meet corporate goals, DataOps strives to continuously update and adjust data models, visualizations, reports, and dashboards.
- DataOps encourages cross-functional collaboration and automation to create rapid, reliable data pipelines that help your company get the most out of its data.
DataOps Can Be Inspired by DevOps
DataOps is the result of a concept of greater collaboration. This is an Agile approach to creating and implementing a data architecture that works with open source tools and frameworks in the real world. The purpose of DataOps is to extract commercial value from massive data.
The commitment to breaking down data silos and focusing on collaboration across teams is what DevOps and DataOps have in common. DataOps, according to Goetz, is a subset of DevOps that comprises personnel of an organization that work with data, such as data scientists, engineers, and analysts. So it’s not so much a case of DataOps vs. DevOps as it is a case of one complementing the other.
DataOps focuses on IT operations and software development teams, and it can only succeed if line-of-business stakeholders collaborate with data engineers, scientists, and analysts. These data professionals discuss how to best use their data to achieve great business outcomes, while line-of-business team members can point to what the company demands.
A number of IT disciplines fall under the DataOps umbrella, including data development, data transformation and extraction, data quality, data governance, and access control, to name a few. There aren’t any dedicated DataOps software products, but there are frameworks and tool sets that support the DataOps methodology.
Is DevOps required for Big Data?
As previously said, Big Data initiatives can be difficult in terms of:
- dealing with big amounts of information
- delivering the project faster in order to keep up with the competitive market or due to stakeholder pressure
- faster response to changes
Traditional approaches, as opposed to DevOps, are ineffective in overcoming this issue. Different teams and team members have always worked in isolation. Data architects, analysts, administrators, and a slew of other experts, for example, are all working on their parts of the project, which slows down delivery.
DevOps, on the other hand, follows the concepts outlined above and brings together all participants from all stages of the software delivery pipeline. It breaks down barriers between jobs, minimizes silos, and makes your Big Data team more cross-functional. This provides a more shared vision of the project’s purpose, in addition to a significant gain in operational efficiency. With all of this in mind, it’s no surprise that Big Data firms are embracing DevOps and incorporating data professionals into the CI/CD process.
Using DataOps to Make Life Easier for Users
By deploying tested, trusted, and monitored data solutions to construct data pipelines that empower business users, DataOps helps to enhance businesswide cooperation. It breaks down barriers between data consumers and IT, fostering a Data Culture across the board. If you’d like to learn more about DevOps and DataOps integrations, please visit Delphix today.
This was a sponsored post.