In 2019, software development firms are embracing DevOps automation to produce high-quality, multifaceted deliverables quickly. A growing number of software developers recognize the opportunity loss that occurs when business units fail to work together to realize shared goals.
DevOps enables big data teams to work in unison with other development units to deliver applications on schedule – and on budget. With the relatively new software development method, information technology (IT) teams, developers and operations leverage automation to bring application concepts to life quickly.
The following are six ways that big data teams leverage DevOps automation to optimize workflows.
1. DevOps Enables Agility
Big data teams use information to find opportunities for improvement. Accordingly, they need the ability to respond to discoveries quickly.
For example, when analysis reveals that a small website change can yield significant results, big data teams need to deploy updates in days – not weeks – to seize the opportunity. DevOps automation enables enterprises to seize opportunities while they’re still relevant.
2. Bringing Big Data Teams Into the Loop
The work required to manage big data usually demands a large talent pool. At any time, any number of team members could need transparent and secure access to the data management environment.
Today, software teams often work from geographically dispersed locations. Platforms such as JFrog enable software development teams to manage an artifact repository on the cloud, on-site or any combination of the two. DevOps tools such as JFrog Artifactory now empower big data teams to unite the continuous integration and delivery pipeline, remain agile and increase productivity.
3. Asset Allocation Made Easy
Work environment consistency is one of the biggest challenges for big data teams. An organization may have tens, hundreds or even thousands of team members working on the same project.
It’s easy for inconsistencies to arise with so many team members working on the same product. Today, automation platforms enable software developers to build consistent work environments, maintain quality control and deliver high-quality applications.
5. Transparency Across the Board
Outside of DevOps, it can prove challenging for organizations to share core datasets for building and testing. Using DevOps methodology and tools, software development firms can construct a centralized repository to share datasets quickly and securely.
DevOps automation software enables project leaders to deploy entire development environments with a few clicks. The ability to share datasets is an essential mechanism to support the work of big data teams for software development.
6. 100% Situational Awareness
DevOps tools give programmers a centralized and complete overview of artifact repositories. By integrating a cloud-based machine data analytics platform such as Sumo Logic with JFrog, for instance, programmers can pinpoint changes, dependencies and potential conflicts quickly. More importantly, lead developers can stay informed in real time about project details and progress.
DevOps automation is a powerful management tool for big data teams. They enable software development firms to simplify repetitive tasks and streamlined developer workflows. With DevOps methods, software developers can cut big data down to size.
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