ScrapeNinja
Extract Web Data on Scale
Integrate the ScrapeNinja API with the Go API
Setup the ScrapeNinja API trigger to run a workflow which integrates with the Go API. Pipedream's integration platform allows you to integrate ScrapeNinja and Go remarkably fast. Free for developers.
Popular ScrapeNinja 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 ScrapeNinja#
ScrapeNinja API on Pipedream allows you to craft powerful serverless workflows for web scraping without the hassle of managing proxies or browsers. It's a tool that can extract data from websites, handling JavaScript rendering and anti-bot measures with ease. By integrating ScrapeNinja with Pipedream, you can automate data collection, collate and process the scraped data, and connect it to numerous other services for further analysis, alerting, or storage.
Connect ScrapeNinja#
import { axios } from '@pipedream/platform';
export default defineComponent({
props: {
scrapeninja: {
type: "app",
app: "scrapeninja",
}
},
async run({steps, $}) {
return await axios($, {
method: 'POST',
url: 'https://scrapeninja.p.rapidapi.com/scrape',
headers: {
'content-type': 'application/json',
'X-RapidAPI-Key': this.scrapeninja.$auth.rapid_api_key,
'X-RapidAPI-Host': 'scrapeninja.p.rapidapi.com'
},
data: {
url:"https://news.ycombinator.com/"
}
})
},
})
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)
}