Bitbucket Cloud is a Git-based code and CI/CD tool optimized for teams using Jira.
Emit new event when a new commit is pushed to a branch. See docs here
Emit new event when a new pull request is created in a repository. See docs here
Emit new event when a new branch is created. See docs here
Emit new event when a commit receives a comment. See docs here
Emit new event when an event occurs from any repository belonging to the user. See docs here
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Gets the actual file contents of a download artifact and not the artifact's metadata. See docs here
The BitBucket API taps the potential of BitBucket's Git-based version control system, enabling you to automate workflows around code commits, pull requests, and overall repository management. With this API, you can streamline the collaboration process, enforce coding standards, or integrate with other tools to create a cohesive development ecosystem. Pipedream, as a serverless integration and compute platform, provides a seamless environment to connect BitBucket with various apps and services, enabling you to harness its API for efficient, customized automations.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
bitbucket: {
type: "app",
app: "bitbucket",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.bitbucket.org/2.0/user`,
headers: {
Authorization: `Bearer ${this.bitbucket.$auth.oauth_access_token}`,
},
})
},
})
Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.
This includes installing PyPI packages, within your code without having to manage a requirements.txt
file or running pip
.
Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:
def handler(pd: "pipedream"):
# Reference data from previous steps
print(pd.steps["trigger"]["context"]["id"])
# Return data for use in future steps
return {"foo": {"test":True}}