The 9 Key steps to implement Big Data DevOps
The 9 Key steps to implement Big Data DevOps !
Per WiKi Definition: DevOps (a clipped compound of development and operations) is a culture, movement or practice that emphasizes the collaboration and communication of both software developers and other information-technology (IT) professionals while automating the process of software delivery and infrastructure changes.
Per Gene Kim(author of The Phoenix Project): DevOps is set of cultural norms and technical practices that enable this fast flow of work from dev through test through operations while preserving world class reliability.
Per Community: DevOps means [caring] about your job enough to not pass the buck. DevOps means [caring] about your job enough to want to learn all the parts and not just your little world.
DevOps is a way of thinking and a way of working. It is a framework for sharing stories and developing empathy, enabling people and teams to practice their crafts in effective and lasting ways. It is part of the cultural weave that shapes how we work and why. Many people think about devops as specific tools like Chef or Docker, but tools alone are not devops. What makes tools “devops” is the manner of their use, not fundamental characteristics of the tools themselves.
Given the current scenario of agility in Big Data application development let’s talk about the 9 Key Steps in Big Data DevOps ?
- Developer Pulls From Trunk
- Make incremental changes on local environment
- Developer pushes commits
- Continuous Improvement(CI) monitors repository for changes
- Changes Kick off test Build
- Run Tests: Unit, Integration, Smoke
- Report back test results
- If test pass Deploy code to artifact repository
- Pushing to repo could just mean changing the reverse proxy server
Reference – Getting Started with DevOps, Cisco.
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