Free Project Planning: We dive into your goals and current state to prepare before a kickoff.
Pay-as-you-go: Use our capacity when you need it, none of that retainer nonsense.
Experts On-Demand: Get new experts from our team when you need specific knowledge or consultation.
We Don't Sleep: Just kidding we do sleep, but we can flexibly hop on calls when you need.
Ad-hoc Calls: When a video call works better than a chat, we hop on a call together.
HashiCorp Packer is a tool designed to automate the creation of machine images across multiple platforms from a single configuration. It simplifies the process of image creation for cloud, virtual, and physical environments by integrating with various builders such as AWS, Azure, and Google Cloud, and provisioning tools like Chef and Ansible. This ensures consistent, repeatable builds, enhancing security and reducing potential errors. Packer supports immutable infrastructure practices by facilitating the use of disposable images, making the deployment process more efficient and manageable. Its extensive documentation and strong community support further aid in its adoption for infrastructure management and deployment tasks.
Continuous Integration is a mode of work where multiple programmers can integrate changes continuously into the same code.
The foundation of successful collaboration lies in the agreement on facts, while the key to achieving development velocity is through conducting experiments in the form of tests to validate the code's functionality.
Continuous Integration facilitates both of these processes by creating two distinct processes:
- The first process allows developers to agree on the "true" codebase, commonly called the master branch or trunk.
- The second process validates the codebase after changes are made using tests.
For startups, it is crucial to have processes in place that enable collaboration, and enhance the delivery of changes in a consistent, predictable, and safe manner. This is typically achieved by running automated tests after the introduction of a change into a Git branch or after creating a Pull-Request. If the tests fail or if the branch is not up-to-date with the latest changes from the main branch, the change to the code cannot be introduced to the main version of the code. Such measures ensure that non-working changes are not introduced into the main branch, instilling confidence in introducing changes to the system.