Zeta (Boomtrain)
Data-Driven Marketing
Integrate the Zeta (Boomtrain) API with the Go API
Setup the Zeta (Boomtrain) API trigger to run a workflow which integrates with the Go API. Pipedream's integration platform allows you to integrate Zeta (Boomtrain) and Go remarkably fast. Free for developers.
Popular Zeta (Boomtrain) 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 Zeta (Boomtrain)#
The Zeta (Boomtrain) API offers a suite of tools designed for predictive marketing and analytics. By leveraging machine learning and data science, it can help personalize user experiences and optimize marketing efforts. With this API on Pipedream, you can automate customer segmentation, execute targeted campaigns, and analyze customer behavior. The API's capabilities can be harnessed to create dynamic, responsive marketing workflows that react to user interactions in real-time.
Connect Zeta (Boomtrain)#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
boomtrain: {
type: "app",
app: "boomtrain",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://events.api.boomtrain.com/events/recent/${this.boomtrain.$auth.site_id}`,
headers: {
Authorization: `Bearer ${this.boomtrain.$auth.id_token}`,
},
params: {
count: `10`,
},
})
},
})
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)
}