Travis CI
The simplest way to test and deploy your projects. Easily sync your projects with Travis CI and you'll be testing your code in minutes.
Integrate the Travis CI API with the Go API
Setup the Travis CI API trigger to run a workflow which integrates with the Go API. Pipedream's integration platform allows you to integrate Travis CI and Go remarkably fast. Free for developers.
Popular Travis CI and Go Actions#
Run any Go code and use any Go package available with a simple import. Refer to the Pipedream Go docs to learn more.
Overview of Travis CI#
The Travis CI API enables developers to automate and enhance their Continuous Integration and Delivery pipeline. With the API, you can manage builds, retrieve build information, cancel jobs, restart builds, and interact with various other Travis CI components programmatically. When you pair this functionality with Pipedream, you can automate reactions to build events, sync data between tools, and trigger workflows in other apps based on Travis CI activity.
Connect Travis CI#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
travis_ci: {
type: "app",
app: "travis_ci",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.travis-ci.com/user`,
headers: {
"Travis-API-Version": `3`,
"Authorization": `token ${this.travis_ci.$auth.api_key}`,
},
})
},
})
Overview of Go#
You can execute custom Go scripts on-demand or in response to various triggers and integrate with thousands of apps supported by Pipedream. Writing with Go on Pipedream enables backend operations like data processing, automation, or invoking other APIs, all within the Pipedream ecosystem. By leveraging Go's performance and efficiency, you can design powerful and fast workflows to streamline complex tasks.
Connect Go#
package main
import (
"fmt"
pd "github.com/PipedreamHQ/pipedream-go"
)
func main() {
// Access previous step data using pd.Steps
fmt.Println(pd.Steps)
// Export data using pd.Export
data := make(map[string]interface{})
data["name"] = "Luke"
pd.Export("data", data)
}