Airtable is a low-code platform to build next-gen apps. Move beyond rigid tools, operationalize your critical data, and reimagine workflows with AI.
Emit new event each time a record is added, updated, or deleted in an Airtable table. See the documentation
Emit new event for each new or modified record in a table
Emit new event for each new or modified record in a view
Run any Go code and use any Go package available with a simple import. Refer to the Pipedream Go docs to learn more.
Create one or more records in a table by passing an array of objects containing field names and values as key/value pairs. See the documentation
Airtable (OAuth) API on Pipedream allows you to manipulate and leverage your Airtable data in a myriad of powerful ways. Sync data between Airtable and other apps, trigger workflows on updates, or process bulk data operations asynchronously. By using Airtable's structured databases with Pipedream's serverless platform, you can craft custom automation solutions, integrate with other services seamlessly, and streamline complex data processes.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
airtable_oauth: {
type: "app",
app: "airtable_oauth",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.airtable.com/v0/meta/whoami`,
headers: {
Authorization: `Bearer ${this.airtable_oauth.$auth.oauth_access_token}`,
},
})
},
})
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.
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)
}