ReleaseTEAM, Inc.

Consider the impact of archive projects on your cloud migration strategy. As discussed previously, the volume of data in your data center directly influences the duration and complexity of your cloud migration. A cleaner, leaner data center environment translates to a faster, smoother, and more successful migration experience. Therefore, it's crucial to evaluate your archive projects and determine their necessity. Phased migration is a viable approach: move active projects first, then revisit archives later, avoiding the pressure of migration weekends. For more on Atlassian Cloud Migrations, visit https://lnkd.in/gMuNaFCA What strategies are you employing to optimize your data for migration? #CloudMigration #DataManagement #Atlassian #ITStrategy #DigitalTransformation

Clear communication is one of the most underrated parts of DevOps and software development. That’s why teams use Perforce Gliffy - to turn complex workflows, architectures, and processes into visuals everyone can understand. Whether you’re mapping infrastructure, documenting systems, or collaborating across teams, diagrams help reduce confusion and keep projects moving faster. The best tools don’t just help teams build better software—they help people stay aligned while doing it. https://bit.ly/40hbgOe

The next chapter for Atlassian, and your teams, is in the cloud. Join our on-demand webinar to learn how leading enterprises are accelerating their journey with Atlassian Ascend - achieving faster timelines, reduced costs, and new levels of AI-powered collaboration.  https://bit.ly/4tkZ5uC

GitLab’s “Act 2” shows the company is thinking far beyond AI code suggestions. The bigger vision is about rebuilding DevSecOps for an era where AI agents can plan, code, review, deploy, and even repair software at machine scale. What stands out is GitLab’s focus on orchestration, governance, and context—not just generating code faster. As AI-driven development grows, platforms that can coordinate workflows securely across the entire lifecycle will matter more than ever. The story is still unfolding, but one thing is clear: the future of DevOps will be shaped by how well humans and AI agents work together. 🔗 Read more: https://bit.ly/4uu92ao

If you’re learning cloud, #DevOps, or #AWS, Stephane Maarek is one of the best people to follow. He has a talent for breaking down complex technical topics into practical, easy-to-understand lessons that actually help professionals level up their skills. Whether you’re studying for certifications, exploring cloud architecture, or trying to stay current with AWS trends, his content consistently delivers real value. Great teachers don’t just share information—they make people more confident in their careers. Stephane does exactly that. 🚀 https://bit.ly/4dA41Gq

GitOps builds on #DevOps principles but with some definite differences. #GitOps introduces a more structured, declarative approach to managing infrastructure and deployments. It uses Git as the single source of truth for both application code and system configuration. How can you use this? What does your organization need? Check out the blog for more! https://bit.ly/49n923B

Please join us! We're inviting YOU to an in-person, hands-on conference for Atlassian admins, engineers, and teams. Live in Dallas, TX. October 27-29, 2026. Get the info here: https://bit.ly/4wsKFLK

Red Hat is showing what the next phase of IT automation could look like. By integrating AI agents with Ansible Automation Platform through MCP support, teams can use natural language to trigger automation workflows—while still keeping governance, RBAC, and security controls in place. That balance matters. Enterprises want the speed and flexibility of AI agents, but they also need trusted execution and operational guardrails. The bigger takeaway? AI in DevOps is moving beyond assistance and toward orchestrated action. 🔗 Read more: https://bit.ly/4d8Jnhy

GitLab Runners are the engine behind your CI/CD pipelines — but misconfiguration can lead to bottlenecks, security risks, and performance issues. In this video, we'll show you: • How GitLab Runners actually work • Shared vs. specific runners • Executor types (Shell, Docker, Kubernetes) • Autoscaling strategies • Security and isolation considerations • Performance tuning tips https://bit.ly/4dlOGc6

AI can speed up development—but only if it fits naturally into your workflow. GitLab Duo brings AI directly into the DevSecOps lifecycle, helping developers write code, explain changes, troubleshoot issues, and improve security without constantly switching tools. The biggest advantage? Context. Because GitLab Duo is built into the platform teams already use, it can provide more relevant assistance across planning, coding, testing, and deployment. Less friction. Faster delivery. Smarter collaboration. https://bit.ly/4uIDQ74