Build a barcode system without code.
Emit new event when a new row is created in an Orca Scan sheet. See the documentation
Emit new event when a new sheet is created in Orca Scan. See the documentation
Adds a new row or updates an existing row in a sheet. See the documentation
Run any Go code and use any Go package available with a simple import. Refer to the Pipedream Go docs to learn more.
Locates a row record based on a given barcode. If no barcode is provided, return all rows. See the documentation
The Orca Scan API is a gateway to integrate barcode scanning and inventory management capabilities into various workflows. By leveraging the API, you can automate data collection, streamline inventory tracking, and sync your barcode scanning data with other systems. When used on Pipedream, you can merge Orca Scan functionalities with countless other apps, creating custom automation rules, processing data, and managing inventory in real-time. Ideal for inventory management, asset tracking, and data collection automation, the Orca Scan API is a tool that can adapt to a myriad of business needs.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
orca_scan: {
type: "app",
app: "orca_scan",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.orcascan.com/v1/sheets`,
headers: {
Authorization: `Bearer ${this.orca_scan.$auth.api_key}`,
},
})
},
})
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)
}