10,000ft by Smartsheet
Simple resource allocation
Integrate the 10,000ft by Smartsheet API with the Go API
Setup the 10,000ft by Smartsheet API trigger to run a workflow which integrates with the Go API. Pipedream's integration platform allows you to integrate 10,000ft by Smartsheet and Go remarkably fast. Free for developers.
Popular 10,000ft by Smartsheet 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 10,000ft by Smartsheet #
The 10,000ft by Smartsheet API opens the door to managing project scheduling, resource allocation, and reporting in a programmatic fashion. This API allows you to automate the retrieval of project data, update assignments, track time and expenses, and generate real-time reports. By leveraging Pipedream's capabilities, you can create custom workflows that respond to events in 10,000ft, synchronize data across multiple systems, and streamline project management tasks.
Connect 10,000ft by Smartsheet #
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
_10000ft: {
type: "app",
app: "_10000ft",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.10000ft.com/api/v1/users `,
headers: {
"auth": `${this._10000ft.$auth.token}`,
},
params: {
"Content-Type": `application/json`,
},
})
},
})
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)
}