With a subtitle of 'Develop, Deploy, and Scale', this practical guide demonstrates how the open source Cloud Foundry application platform can reduce the develop-to-deploy cycle time. Author Duncan C. E. Winn argues that it also raises the value line for application operators by changing the way applications and supporting services are deployed and run.
<ASIN:1491932430>
The book shows DevOps and operations teams how to configure and run Cloud Foundry at scale. It covers Cloud Foundry’s technical concepts—including how various platform components interrelate—as well as guideance on choosing your underlying infrastructure, defining the networking architecture, and establishing resiliency requirements.
Author: Duncan C. E. Winn Publisher: O'Reilly Date: June 2017 Pages: 324 ISBN: 978-1491932438 Print: 1491932430 Kindle: B071SG9SFG Audience: DevOps Level: intermediate Category: Cloud Computing
Contents include:
- Cloud-native concepts that make the app build, test, deploy, and scale faster
- How to deploy Cloud Foundry and the BOSH release engineering toolchain
- Concepts and components of Cloud Foundry’s runtime architecture
- Cloud Foundry’s routing mechanisms and capabilities
- The platform’s approach to container tooling and orchestration
- BOSH concepts, deployments, components, and commands
- Basic tools and techniques for debugging the platform
- Recent and soon-to-emerge features of Cloud Foundry
Follow @bookwatchiprog on Twitter or subscribe to I Programmer's Books RSS feed for each day's new addition to Book Watch and for new reviews.
To have new titles included in Book Watch contact BookWatch@i-programmer.info
Seriously Good Software
Author: Marco Faella Publisher: Manning Date: March 2020 Pages: 328 ISBN: 978-1617296291 Print: 1617296295 Kindle: B09782DKN8 Audience: Relatively experienced Java programmers Rating: 4.5 Reviewer: Mike James Don't we all want to write seriously good software?
|
Artificial Intelligence, Machine Learning, and Deep Learning (Mercury Learning)
Author: Oswald Campesato Publisher: Mercury Learning Date: February 2020 Pages: 300 ISBN: 978-1683924678 Print: 1683924673 Kindle: B084P1K9YP Audience: Developers interested in machine learning Rating: 4 Reviewer: Mike James
Another AI/ML book - is there room for another one?
| More Reviews |
|